Use of new technologies to evaluate the environmental footprint of feedlot systems

Abstract

With increased concern over the effects of livestock production on the environment, a number of new technologies have evolved to help scientists evaluate the environmental footprint of beef cattle. The objective of this review was to provide an overview of some of those techniques. These techniques include methods to measure individual feed intake, enteric methane emissions, ground-level greenhouse gas and ammonia emissions, feedlot and pasture emissions, and identify potential pathogens. The appropriate method to use for measuring emissions will vary depending upon the type of emission, the emission source, and the goals of the research. These methods should also be validated to assure they produce accurate results and achieve the goals of the research project. In addition, we must not forget to properly use existing technologies and methods such as proper feed mixing, feeding management, feed/ingredient sampling, and nutrient analysis.

INTRODUCTION

The potential adverse effects of animal-feeding operations (AFOs) on the environment are of growing concern. The air pollutants of most concern to beef cattle operations from a regional and national standpoint are ammonia and greenhouse gases (GHG: methane and nitrous oxide); whereas, particulate matter, volatile organic compounds (VOCs), and pathogens tend to be a local concern.

Gaseous emissions from AFO are formed by a complex set of microbial, physical, and chemical processes that occur within the animal and the manure storage/processing system. Appreciable spatial and temporal variation can occur in gaseous emissions because of differences in the animals, the diets, manure storage/handling systems, and the environment (NRC, 2003; Powers et al., 2014; NASEM, 2016). It is imperative that we develop a better understanding of the effects of AFO on gaseous emissions, so they will be regulated based on science and to develop potential solutions to air-quality concerns. Therefore, the objectives of this review were to provide an overview of techniques that can be used by researchers to evaluate the environmental effects of cattle feeding and grazing operations.

No animals were used in this review; therefore, no Institutional Animal Care and Use Committee approval was required.

EMISSIONS OF CONCERN

Ammonia (NH₃)

The main source of ammonia on feedlot surfaces is urine spots where urea in urine is rapidly hydrolyzed to ammonia and carbon dioxide (Varel, 1997). Ammonia emissions are controlled by dietary protein content, stage of feeding, temperature, surface pH, moisture content, bulk density, wind speed/turbulence, and ammonia concentration in the air above the source (Whitehead and Raistrick, 1991; Ni, 1999; Cole et al., 2006).

Greenhouse Gases

Methane at feedlots is produced by normal ruminal fermentation and fermentation of manure on pen surfaces and in stockpiles. The majority of methane emissions from feedlots appear to come from enteric fermentation (Powers et al., 2014; USEPA, 2015).

Nitrous oxide is produced via nitrification and denitrification on the pen surface. There is little information on nitrous oxide emissions from AFO; however, the quantity of nitrous oxide emitted from soils depends on soil temperature and on the quantity of nitrate, carbon, water, and oxygen in the soil (Waldrip et al., 2016).

Bioaerosols/Pathogens

Bioaerosols can potentially contain a variety of bacterial, viral, and fungal pathogens, as well as microbial byproducts such as endotoxin (Eduard, 1997; Wilson et al., 2002). These micron-sized biological particles can be liquid, semi-solid, or solid (Dungan, 2010) and are a potential health risk to humans (and livestock) if inhaled or ingested (Van Leuken et al., 2016).

THE “BASICS” OF MANY NEW TECHNOLOGIES

Two technological developments over the past 10+ yr have allowed beef cattle researchers to easily obtain data that were previously very difficult or impossible to obtain. The first was the development of radio frequency identification tags (RFID) and readers that use electromagnetic fields to identify and track tags on the animal.

The second basic technology developed has been systems to measure DMI of individual animals in a pen setting. There are at least four systems on the market today with costs that range from about $500 to $1,000 per animal capacity. The original system developed was the Calan Broadbent gate system (American Calan, Northwood New Hampshire). This system does require labor for feeding and for animal training, and about 10% of animals do not train to the system. However, the major advantage is that animals within the same pen can be fed different diets and/or be limit fed. More recently, the GrowSafe (GrowSafe Systems Ltd, Airdrie, Alberta, Canada), Insentec (Insentec, Marknesse, Netherlands), and Smartfeed (C-Lock, Rapid City, SD) systems have been developed (Reuter et al., 2017) to measure DMI electronically. Several of these systems can limit access to feed if limited feeding is desired.

SYSTEMS TO MEASURE ENTERIC METHANE PRODUCTION

Available methods to measure enteric methane emission from ruminants were recently reviewed in detail (Hammond et al., 2016). They will be briefly reviewed here.

Respiration Chambers

Respiration chambers have been used in cattle nutrition research to measure energy metabolism and gas (methane and carbon dioxide) production for over a century (Blaxter, 1962). The most common type of respiration chamber used has been the open circuit, indirect system. The design of chambers and gas collection/analysis equipment have varied from location to location and have changed as technologies have improved, but the basic principles have remained the same (Blaxter, 1962; Cole et al., 1975; Nienaber and Maddy, 1985; McGinn et al., 2004; Hales et al., 2014; Shreck et al., 2017). Typically, a negative pressure is created by pulling air from the chamber at a rate sufficient to maintain safe breathing conditions for the animal (approximately 1% carbon dioxide) while causing a sufficient change in gas concentrations so the gas emissions and consumption can be accurately measured. The gas concentrations in the incoming and outgoing air may be measured continuously or semicontinuously at designated intervals. In addition, or in lieu of continuous/semicontinuous air sampling, a representative sample of the air may be collected over a designated time and then be analyzed. Chambers require regular validation using a mass recovery technique such as burning propane or ethanol (Lighton, 2008) or by releasing known quantities of a specified gas.

Some recent modifications

With the increased interest in environmental issues, several institutions have built modified systems to measure GHG, ammonia, and other gaseous emissions. At Michigan State University (MSU), Chiavegato et al. (2015) used environmental chambers that are larger than typical respiration chambers and can house multiple animals. In the MSU system, the feces and urine can be retained in the chamber so that emissions from the manure can also be estimated. Researchers at the University of California-Davis constructed soil-surfaced cattle pen enclosures made of a nonpermeable polymer that can house 10 cattle or more. Feces and urine are deposited on the soil surface, and thus, emissions from both animals and the pen surface can be estimated (Stackhouse-Lawson et al., 2013).

The advantages of respiration chambers are their ability to accurately measure GHG emissions from individual animals. However, these measurements are normally obtained for short periods (1 to 5 d), and animal activity is limited. Appreciable animal training is also required, and DMI is typically less than seen in practical situations. The larger chambers can overcome some of these disadvantages; however, they can suffer from some of the same challenges that occur with small ground-level flux chambers discussed in the next section. In all systems, it is imperative that the air sampled and analyzed is representative of all the air because gas consumption and production are not constant over a 24-h period (Hales and Cole, 2017).

The Sulfur Hexafluoride (SF-6) Method

The Sulfur Hexafluoride (SF-6) method was developed by Johnson et al. (1994) to overcome many of the limitations of respiration chambers. In the SF-6 method, the animal receives a permeation tube that releases SF-6 into the rumen over an extended period. The estimated release rate of SF-6 from the permeation tube is determined in vitro before it is given to the animal. A sample of gases emitted from the nostrils is collected in evacuated gas canisters mounted on the animal using an air sampling tube located near the animal’s nostril. The air in the gas canisters is analyzed for methane, carbon dioxide, and SF-6, and methane emissions are calculated from the SF6:methane ratio in the collected gas and the predetermined SF-6 release rate.

The advantages of the SF-6 method are that animals have access to their normal environment and that DMI is not limited. However, release of SF-6 from permeation tubes can be inconsistent, and the technique requires significant training and labor. Also, gas accumulations within barns can artificially increase the background gas concentrations and potentially bias the results. Although results using SF-6 are more variable than respiration chambers, average values are similar, and it is possible to sample more animals in a near-normal environment than using respiration chambers (Boadi et al., 2002; Jonker et al., 2016).

The GreenFeed Emission Monitoring System

The GreenFeed Emission Monitoring System (GEM: C-Lock) was developed as an alternative to respiration chambers and the SF-6 method. It consists of a head box, a feeder, air handling system, and gas analyzers. A bait feed is used to entice the animal to use the system. The timing and quantity of the bait deliveries can be programmed. The GEM system has been evaluated under various conditions (Hammond et al., 2015; Jonker et al., 2016; Alemu et al., 2017; Arthur et al., 2017; Gunter et al., 2017). A summary of the GEM evaluation by Jonker et al. (2016) is presented in Table 1. In general, when animals are properly trained, and sufficient numbers of samples are obtained (preferably more than 30/animal), and bait feeding conditions are set correctly, the GEM system appears to give accurate results. With the GEM system, the animal is maintained in its normal environment with near-normal DMI. Less training and labor are required than with other methods. The system appears to be relatively easy to maintain and can take long-term measurements over months.

Table 1.

Effects of measurement technique and feeding pattern on DMI and enteric methane emissions of eight heifers fed grass silage-based diets (Jonkers et al., 2016)

	Perioda
Itemb	P1	P2	P3	P4	P5	SED	P<
DMI, kg/d
Chamb.	5.9	7.5	8.3	8.3	10.9	0.30	0.001
SF6	5.9	7.1	8.4	8.2	12.2	0.27	0.001
GEM	5.9	7.2	8.3	8.2	12.1	0.22	0.001
SED	0.01	0.13	0.07	0.07	0.35
P<	0.99	0.005	0.68	0.23	0.08
CH4, g/d
Chamb.	141	184	205	198	265	6.2	0.001
SF6	134	157	189	197	272	12.1	0.001
GEM	144	177	220	236	323	12.7	0.001
SED	7	6	10	12	24
P<	0.32	0.002	0.017	0.005	0.002

^aAnimals were fed once daily in P1 and P2, respectively, either three or four times per day in P3 and P4, respectively, and had ad libitum access to feed during P5. SED = standard error of the difference.

^bChamb = methane production determined in respiration chambers. SF6 = methane production determined using the SF6 method. GEM = methane production determined using the GreenFeed system (C-Lock).

At the USDA-ARS, Bushland, TX, the GEM system is used with the Calan gate system (Ebert et al., 2017). Once trained to the systems, 27 to 36 cattle can be merged so that each animal has access to its individual feeder and the GEM. Australian scientists have successfully used the GEM system with the GrowSafe system (Arthur et al., 2017). However, first attempts were problematic because the RFIDs used to run the systems were not compatible (Roger Hegarty, personal communication). Researchers who wish to use systems that require multiple RFID should check the compatibility of the RFID beforehand.

SYSTEMS TO MEASURE PEN AND RETENTION POND EMISSIONS

It is often desired to measure gaseous emissions originating from the feces and urine deposited on the pen surface or from runoff collected in retention ponds or lagoons. Gas production and volatilization from ground-level sources are a complex combination of biochemical and physical processes that require a gas source, a concentration gradient between the surface and the atmosphere, and the physical removal of the gas by atmospheric turbulence. Techniques for experimentally estimating these emissions include flux chambers, micrometeorology methods (MM), mass balance, and models. No matter the method used, it is imperative that the livestock operation (e.g., number of animals, area, management, diet, animal age and type, health, housing type, etc.) and environmental conditions be thoroughly documented. Preferably, multiple methods should be used, and, when feasible, a complete nutrient balance should be calculated to assure the emission values are reasonable.

Flux Chambers and Wind Tunnels

Enclosure methods to assess emissions from ground-level sources include chambers that completely isolate an emitting surface and wind tunnels that partially enclose and restrict an emitting surface. Chambers are generally classified as either non-steady-state (NSS) or steady-state (SS) (Rochett and Hutchinson, 2005).

Non-flow-through-non-steady state chambers (NFT-NSS) are frequently used to measure GHG emissions from fields (Venterea, 2013) or pen surfaces (Casey et al., 2015). Typically, the chamber is affixed to the surface, and gas samples from within the chamber are obtained two to four times over 30 to 60 min and analyzed for the gases of interest. The rate of increase in gas concentration, determined using linear or nonlinear regression, is the flux rate. Because many different sized chambers and sampling intervals have been used, comparisons across experiments are frequently difficult. Parkin and Venterea (2010) and de Klein and Harvey (2012) have recommended standard operating procedures for the use of NFT-NSS chambers to measure GHG emissions from fields.

In the flow-through-steady state (FT-SS) flux chambers, ambient air is pulled through the chamber at a known constant rate to a gas analyzer that continuously measures gas concentrations. After 5 to 10 min, the gas concentrations within the chamber normally “plateau” and that concentration is used to estimate a flux rate (Parker et al., 2010, 2013). Unfortunately, the peak gas concentration, and thus the flux rates of many gases (ammonia, VOCs), is dependent upon the air flow rate (Parker et al., 2010, 2013) and typically will increase as air turnover rates increase to approximately 15 turnovers/min (Whitehead and Raistrick, 1991; Cole et al., 2007). In most FT-SS chambers, air is pulled through the chamber at a low rate (<1 turnover/min), which allows gases to accumulate within the chamber, thus altering the microenvironment and interfering with normal emission processes. In some cases, the temperature in the chamber may increase substantially, increasing the flux rate, and partially hiding the error caused by the low flow rates.

Wind tunnels partially enclose a source area, typically with open ends so that forced or natural air movement is possible (Meisinger et al., 2001). Concentrations of gases in the inlet and outlet air are measured along with air flow rate through the wind tunnel to determine the quantity of a gas entering and leaving the wind tunnel—the difference is the flux rate. When wind speeds through wind tunnels are similar to environmental wind speeds, the measured emissions are similar (Loubert et al., 1999).

As previously noted, chambers have several serious (but frequently ignored) problems when measuring many gaseous emissions. For example, Sommer et al. (2004) noted that emissions of GHG measured from manure stockpiles using NFT-NSS chambers were only 12% to 22% of flux measured using a MM. Similarly, Cole et al. (2007) noted that ammonia emissions measured under controlled conditions using FT-SS were less than 10% of the uninhibited flux rate. In general, flux chambers and wind tunnels are appropriate for comparing treatments or assessing relative emission rates, but not for quantifying actual emissions. In addition, emissions of some gases are concentrated in small areas (i.e., ammonia from urine spots); therefore, many measurements (more than 100/pen) must be made to account for spatial variability (Cole et al., 2007).

To overcome many of these problems, Parker et al. (2017a) developed a recirculating flow-through-NSS chamber to measure emissions of ammonia and GHG from ground-level sources. Air is pulled from the chamber to the gas analyzer where concentrations are measured continuously (at 1-s intervals for 60 s), and the air is then recirculated to the chamber. The flux is calculated from the linear increase in gas concentrations. The system has been validated for nitrous oxide emissions and is currently being tested for methane and ammonia emissions. The method was more repeatable, faster, and more precise than other chamber methods.

Chambers should be validated to assure they provide accurate measurements of emissions and/or to develop correction factors. To validate flux rates, a surrogate gas source that is affected by the same factors that affect natural emissions (pH, temperature, air turbulence, etc.) should be used. Meisinger et al. (2001) and Cole et al. (2007) noted that ammonia-N losses from a buffered ammonium sulfate solution were very sensitive to pH and that by adjusting the pH of the solution, the flux from the ammonia source could be modified to match typical emission rates from fields or feedlot pen surfaces.

Micrometeorological Methods

Micrometeorology methods do not interfere with the processes of emissions, integrate emissions over large areas, and allow controlled measurements over extended time periods (Fowler et al., 2001; Meyers and Baldocchi, 2005; Baker and Kimball, 2010). They have been successfully used to measure emissions from pastures (Bussink et al., 1996), AFOs (Hutchinson et al., 1982; McGinn et al., 2006; Flesch et al., 2007; Todd et al., 2008, 2014; Sun et al., 2015), and simulated feedlot surfaces (Todd et al., 2006). However, because MM require large, relatively homogenous land areas, replicated comparison of treatments or mitigation strategies is often not possible. In addition, MM can be expensive and/or require significant labor.

Quantifying gaseous emissions using MM requires two components: 1) accurate measurement of atmospheric gas concentration and 2) estimation of gaseous transfer to the atmosphere based on direct measurement or on a flux model that describes or simulates the turbulent dispersion of gases.

Measuring atmospheric gas concentrations

Instruments and techniques to measure ambient atmospheric gas concentrations at open lot beef cattle feedlots or on pastures must be able to detect lower concentrations than those encountered in enclosed confinement systems. For example, background ambient atmospheric ammonia concentrations typically range from <1 to 40 µg m⁻³ (Todd et al., 2006), and maximum ammonia concentrations in air over feedlots rarely exceed 3,000 µg m⁻³ (Todd et al., 2008). Thus, determination of gas concentrations often requires highly sophisticated and expensive equipment and (or) considerable labor. Also, because AFO can have high dust concentrations, methods to measure ammonia and GHG must be either unaffected by the dust, or a method to remove the dust, such as a prefilter, must precede the detector. Because of large spatial and temporal variability, concentration measurements should be taken over extended periods of time and include all the annual seasons. Gas concentrations can be obtained at a single point or averaged over an extended line using open-path technologies.

Spot gas concentrations: ammonia

Atmospheric ammonia concentrations are typically determined using one of the three broad methods: 1) chemical acid absorption (gas washing), 2) optical absorption, and 3) chemical transformation. Ammonia readily adsorbs to many surfaces; therefore, any sampling lines must be as short as possible to avoid loss of ammonia from the sample. Gas washing/acid scrubbing is a proven, relatively inexpensive, and accurate method to measure atmospheric ammonia (Todd et al., 2006). With careful processing of samples and a sensitive laboratory analyzer, the method can detect concentrations as low as 5 µg m⁻³. However, it is labor intensive and requires sample integration times on the scale of hours. Passive absorptive devices (Rabaud et al., 2001; Scholtens et al., 2003) trap ammonia on acid-impregnated surfaces, which are subsequently extracted and analyzed. Passive samplers rely on wind and diffusion to convey ammonia to the absorbing surface, so the minimum detection limits (50 to100 µg m⁻³) are higher than those for active gas washing. Other methods used to detect atmospheric ammonia include photoacoustic infrared spectroscopy, chemiluminescence, gas chromatography, detector tubes, annular denuders, and electrochemical cells (Harper, 2005).

Spot gas concentrations: GHG

Spot methane concentrations are routinely measured using infrared analyzers or gas chromatography. Nitrous oxide is normally measured via gas chromatography using an electron capture detector. These techniques have been used for both ambient air samples, for grab samples captured in canisters and for samples from flux chambers.

Open-path analyzers

The light beam of open-path analyzers operates noninvasively along an open path, and gas concentration is averaged along that path, in contrast to measurements at a single point in space. Ammonia and many GHG can be measured using Fourier transformed infrared (Griffiths et al., 2009) and tuned diode lasers (McGinn et al., 2006; Flesch et al., 2007; van Haarlem et al., 2008; Todd et al., 2008, 2014). Continuous, real-time, measurement is also possible, and minimum detection limits are within the range observed at feedlots. Instrumentation is relatively expensive and requires careful maintenance and calibration. Dust, which is common in feedlots, can sometimes degrade an instrument’s signal.

Overview of MM

Each of the MM make assumptions regarding uniformity of source strength, plume heights, horizontal uniformity of air flow, horizontal concentration gradients, and vertical flux (Wilson and Shum, 1992; Meyers and Baldocchi, 2005). When these assumptions are violated, flux measurements may be biased (Wilson et al., 2001). Therefore, each method has advantages, disadvantages, and appropriateness of use. The MM selected will depend upon factors such as the goal of the experiment, the terrain, and sampling conditions.

Micrometeorological methods that have been used to measure gas emissions from real or simulated livestock operations include eddy covariance (Fowler et al., 2001; Prajapati and Santos, 2017), relaxed eddy accumulation (Sun et al., 2015), integrated horizontal flux (Todd et al., 2006), box models (Cassel et al., 2005; Cole et al., 2011), and the flux-gradient method (Fowler et al., 2001).

Dispersion models

Dispersion models can be used to determine the flux rate of a gas based on downwind gas concentrations or to predict downwind gas concentrations when the flux rate is known. They are based on a mathematical description of the relationship between a source of a gas and a downwind receptor or point using assumptions about turbulent flow (Wilson et al., 2001). Gaussian plume models such as AERMOD used by USEPA are an example of a dispersion model, but their reliability under agricultural conditions has been questioned (Faulkner et al., 2007; Asadi et al., 2017). The backward Lagrangian Stochastic (bLS) model, which has been frequently used in research studies, estimates flux of a gas by modeling the trajectories of thousands of gas particles backward to the emitting source (Flesch et al., 1995, 2005). The bLS model requires a small number of inputs, has been validated for estimating fluxes with gas release experiments (Flesch et al., 1995, 2004), and has been successfully applied to cattle feedlots (McGinn et al., 2006; Flesch et al., 2007; van Haarlem et al., 2008; Todd et al., 2008, 2014). In general, values determined using one dispersion model cannot be used in different dispersion models.

Corroborating Mass Balance Data

When emissions of a gas constitute a large majority of the N or C losses, emissions of those gases can be supported or even estimated indirectly by using a total nutrient balance for the animal production system. This is particularly true for ammonia emissions from research (Buttrey et al., 2012; Luebbe et al., 2012) and commercial (Cole and Todd, 2009) feedlots. Nitrogen contained in the feed, animals, feces, urine, removed manure, soil, and runoff is measured/estimated, and unaccounted N is assumed to be lost as ammonia-N.

A second indirect approach to estimate ammonia emissions is the use of the N:P ratio of the diet and the aged/air-dried manure on the pen surface (Cole et al., 2006; Todd et al., 2008; Buttrey et al., 2012). Because nitrogenous gases volatilize from manure, but P does not, the N:P ratio decreases over time, and the decrease can be used to estimate total volatile N losses. Other minerals (K, total ash) may also be used (Hristov et al., 2009). Todd et al. (2008) noted that feedlot ammonia-N losses based on manure N:P were similar to losses measured using the bLS method. However, the N:P ratio technique has limitations. Contamination of aged/air-dried manure samples with fresh urine or feces will bias the estimates. Buttrey et al. (2012) reported that the use of spot manure sample N:P ratio gave N volatilization losses different from total manure collection. However, the N:P ratio of collected manure gave values very similar to those for total manure collection, indicating the N:P ratio will give good estimates if the manure samples are carefully obtained and representative of the manure on the pen surface.

USING MODELS TO ESTIMATE EMISSIONS

Pen Surface Models

A number of empirical, statistical, and mechanistic models have been developed to estimate nutrient excretion (Waldrip et al., 2013a; Dong et al., 2014; Jiao et al., 2014; NASEM, 2016), and emissions of ammonia (Ni, 1999: Todd et al., 2013) and GHG (IPCC, 2006: Powers et al., 2014) from AFO.

The Manure-Denitrification and Decomposition (Manure-DNDC) model (Li et al., 2012) is a mechanistic model that estimates gaseous emissions from livestock manure systems. The Integrated Farm System Model (IFSM; Rotz et al., 2016) is a combined mechanistic and empirical model that estimates emissions from components of a whole farm system. Waldrip et al. (2013b, 2014) reported that both models could predict the effects of environmental and dietary conditions on ammonia emissions and gave reasonable mean values; however, the predicted and actual values varied considerably in some cases.

Enteric Methane Models

Many empirical models have been developed to predict methane emissions from beef cattle based on diet characteristics (Powers et al., 2014; Escobar-Bahamondes et al., 2017). Unfortunately, results are quite variable, especially for cattle fed high-concentrate diets. Models developed using the same database can estimate very different methane emission from cattle fed the same diet (Table 2). Existing mechanistic models appear to work well for dairy cows but may overestimate methane emission from feedlot cattle (Baldwin, 1995; Kebreab et al., 2008).

Table 2.

Effects of empirical equation and diet quality on predicted methane production (MJ/d) when equations were developed from the same data set (Ellis et al., 2007)

Equation	Variables in modela	Feedlot diet	Fresh alfalfa	Alfalfa hay	Barley straw
1b	MEI	11.50	9.13	7.79	6.88
2b	DMI	9.29	8.73	7.89	7.33
3b	Forage	5.28	9.71	9.71	9.71
5b	NDF	7.11	8.32	8.07	9.24
6b	ADF	6.91	9.42	8.96	9.93
7b	NDF	9.00	8.64	7.56	8.09
8b	ADF	8.85	9.17	7.96	8.09
9b	MEI, Forage	8.04	10.14	8.80	7.88
10b	DMI, Forage	5.93	9.58	8.56	7.88
12b	DMI, Fat	8.32	10.55	9.11	8.71
Mean		8.02	9.34	8.44	8.37
SD		1.81	0.69	0.70	1.00
CV		22.53	7.38	8.23	11.97

^aMEI = metabolizable energy intake, MJ/d; DMI = kg/d; NDF = neutral detergent fiber intake, kg/d; ADF= acid detergent fiber intake, kg/d; Starch = starch intake, kg/d; Forage = %forage in diet; Fat = fat intake, kg/d.

A modified IPCC (2006) method was developed to predict enteric methane emissions from feedlot cattle based on diet characteristics (Powers et al., 2014). The model uses a baseline methane production potential (Ym) of 3% of GE intake (IPCC, 2006) then adjusts the Ym based on grain source, grain processing, grain content of the diet, dietary fat content, roughage concentration, and feeding of an ionophore. Richard W. Todd (personal communication) reported that the Powers et al. (2014) method tended to accurately predict methane emissions from a commercial feedlot in Texas; however, the other empirical models tested tended to greatly overestimate methane emissions from feedlot cattle.

MEASURING PATHOGENS IN BIOAEROSOLS WITH MOLECULAR TECHNIQUES

A number of techniques are available to capture bioaerosols and many have been used at AFOs (Dungan and Leytem, 2009). These have been reviewed by Grinshpun et al. (2016). Despite the ability to capture bioaerosols, less than 10% of aerosolized bacteria form colonies when plated on solid media (Heidelberg et al., 1997).

To overcome the limitations associated with detecting and enumerating viable but nonculturable pathogens, the use of culture-independent molecular approaches can be used to detect, characterize, and quantify pathogens in feedlot air, manure, and water. Before molecular analysis, nucleic acids are extracted from aerosol samples, but results can vary depending upon the DNA extraction kit utilized (Mbareche et al., 2017). In some cases, extraction is not necessary because nucleic acid from target organisms (e.g., DNA virus) can be liberated during the initial polymerase heat activation step (Qiu, 2012).

In bioaerosol studies, some commonly used methods to detect and quantify gene targets of microorganisms, including those of pathogens, are traditional (endpoint) PCR and quantitative real-time PCR (qPCR; Alvarez et al., 1995; An et al., 2006; Fallschissel et al., 2009; Lecours et al., 2012). qPCR has considerable advantages over traditional PCR, including higher specificity and sensitivity, faster detection, no post-PCR analysis, and ability to provide quantitative results (Aw and Rose, 2012). Both traditional PCR and qPCR are commercially available, and cost per reaction is relatively inexpensive (<$1 USD).

The amplified DNA obtained via traditional PCR can be processed further using other molecular tools such as clone library analysis, fingerprinting techniques (e.g., denaturing gradient gel electrophoresis, terminal-RFLP), and microarray analysis (Dungan and Leytem, 2009). These techniques are not quantitative but do provide a means to estimate microbial community structure and diversity, with microarrays also being used to perform functional gene analyses.

Unlike PCR and microarray methods which are limited by known sequence information, high-throughput sequencing technologies make it possible to detect novel pathogens (Aw and Rose, 2012). As a result, the diversity, population size, and potential pathogenicity of microbial communities in complex environments can be characterized based on 16S rRNA amplicons (Yoo et al., 2017). Metagenomics is the study of microbial communities based on genetic material recovered from environmental samples without cultivation, followed by 16S rRNA amplicon sequencing or whole-genome shotgun sequencing (Zoetendal et al., 2004) and makes it possible to assess the entire microbial community of the gut, soil, and other environmental samples. However, significant challenges still exist for its successful use on bioaerosol samples (Behzad et al., 2015).

Molecular biology tools have increased our understanding of microbial diversity and aided in detecting novel genes involved in nutrient digestion, ruminal fermentation, and production of GHG. However, to date, increases in production efficiency and decreases in GHG or ammonia emissions resulting from manipulation of ruminal or fecal microbial community have yet to come to fruition. However, the potential possibilities are great.

CONCLUSIONS

A number of new technologies have evolved over the past decade to allow scientists to evaluate the environmental footprint of beef cattle. Before using these technologies, scientists should evaluate them, to be assured they are obtaining accurate results and achieving the goals of the research project. In addition, in the race to use these new technologies, we must not forget to properly use existing technologies and methods such as proper feed mixing, feeding management, feed/ingredient sampling, and nutrient analysis.

Conflict of interest statement. All authors declare they have no conflict of interests.

Contribution from the USDA-ARS Conservation and Production Res. Lab, Bushland, TX 79012 in cooperation with Texas A&M AgriLife Research. Mention of trade names or commercial products in this article is solely for the purpose of providing scientific information and does not imply recommendation or endorsement by the USDA. Invited presentation at the Research Technology Symposium held at the joint ASAS-CSAS Annual Meeting in Baltimore, MD, July 10, 2017.