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USDA-ARS, U.S. Meat Animal Research Center, P.O. Box 166, Clay Center, NE 68933-0166
* Corresponding author (miller{at}email.marc.usda.gov)
Received for publication March 18, 2004.
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: DM, dry matter • FSM, feedlot surface mixture • GHG, greenhouse gas • MT, moisture threshold • OM, organic matter • VFA, volatile fatty acid • VFACOOH, volatile fatty acid (protonated form)
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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A standard feedlot management objective is to maintain a 2.5- to 5.0-cm layer of well-compacted manure above the mineral soil to enhance drainage, limit seepage, and decrease stress-related leg injuries in cattle. Excess manure is then scraped off the surface between cattle production cycles, usually once or twice a year. Manure enrichment, compaction, and moisture contents all vary considerably across the pen surface and with time during the production cycle (Woodbury et al., 2001) and depend predominantly on the location of feed and water sources. Thus, treating the pen surface as a single, homogenous source oversimplifies its complexity and hinders the development of methods that minimize feedlot emissions.
Odor, NH3, dust, GHG, and pathogen emissions will be related to one another due to similar formation and transport processes. For instance, odor compounds and GHGs are the byproducts of microbial activities (Mackie et al., 1998) utilizing the substrates found in the manure (Miller and Varel, 2003). Ammonia emissions show a close relationship to odor (McGinn et al., 2003) and are also affected to some degree by microbial activities; the conversion of urea in urine to NH4+ is catalyzed by the enzyme urease in the feedlot surface (Muck and Richards, 1980; Varel et al., 1999). Environmental conditions, such as temperature, moisture, and manure content, will influence microbial activities and can directly modify gaseous flux rate and dust formation. Manipulating the feedlot surface moisture through sprinkler irrigation rate or varying stocking density to maintain the moisture content within a 20 to 41% (total mass basis) range has been recommended to control dust at cattle feedlots (Auvermann and Romanillos, 2000; Sweeten et al., 1988; Sweeten, 1998). Modifying cattle behavior relative to feeding schedule may also limit dust emissions (Wilson et al., 2002). Clarifying the effects that temperature, moisture, and manure content have on odor, NH3, dust, and GHG emissions over a range of values would be a critical component establishing cattle feedlot practices that minimize environmental impact.
Although our knowledge of factors influencing individual emissions is expanding, the broad context of multiple emissions and their interactions, from a systems perspective, is lacking. Moisture content and the potential for dust or odor emission have been recognized, but not thoroughly described. Secondary effects of moisture manipulation for dust or odor control on feedlot surface soil N losses, GHG production, and pathogen persistence have not been investigated. The objective of this initial study was to quantify the effect of feedlot surface moisture in three manure and soil mixtures on odor production, dust potential, GHG emissions, and soil N losses under defined laboratory conditions. A companion study (Berry and Miller, 2005) reports the effect of manure and moisture content on pathogenic bacteria and indicator microorganisms.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Pan Incubations
Three feedlot surface mixtures (FSM) containing dried manure (5, 25, and 75% manure on a DM basis with dried soil accounting for the remaining DM) were evaluated. The manure levels contained 99, 235, and 590 g OM kg–1 DM, respectively, similar to observed feedlot OM values (Woodbury et al., 2001). Incubations were conducted in plastic pans (18 cm long x 12.5 cm wide x 8.5 cm deep). Six target moisture levels of 0.11, 0.25, 0.43, 0.67, 1.00, or 1.50 g H2O g–1 dry FSM were tested. Pans contained 360, 320, 280, 240, 200, or 160 g dry FSM with 1 g of powdered urea added per kilogram of dry FSM. In the 25% manure level incubation, all pans inadvertently received varying amounts of urea, which was mixed into the H2O, and not the FSM, before incubation. Water was mixed into each of the pans to reach a final mass of 400 g. The depth of moistened FSM in the pans was roughly 4 cm, and the bulk density varied from 270 to 1200 kg m–3, similar to observed feedlot values (Woodbury et al., 2001). Three replicate pans were prepared for each manure and moisture content. These were then incubated at room temperature (18–22°C) for a period of two weeks under mosquito netting to avoid possible transport of pathogens by insects in the incubation room. To maintain the FSM target moisture content, additional water was thoroughly mixed into the FSM daily based on the mass loss from the previous day. Gas samples for H2, O2, CO, CH4, N2O, and CO2 were collected on Days 0, 1, 2, 3, 7, and 14 in 3-mL syringes 1 h after sealing each of the pans with lids (headspace volume = 1.4–1.7 L) equipped with rubber septa. (Uniform gas emissions were observed in initial incubations at 10-min intervals over 1 h.) Background air samples from the incubation room were also collected in syringes. Immediately following the collection of gas samples, FSM moisture content was adjusted to the target moisture content. Three, 2-g (wet wt.) FSM samples were collected from each pan for dry and organic matter, KCl-extractable N content, and odor compounds and fermentation products and either immediately processed (DM, OM, and N content) or frozen (odor compounds and fermentation products) for subsequent analysis. A fourth 4-g FSM sample was also collected for pH and microbial substrates and was frozen after pH determination.
Sample Analysis
Sample DM and OM contents were determined according to Nelson and Sommers (1996). The pH was determined using a PHM 83 pH meter (Radiometer, Copenhagen, Denmark) after mixing the 4-g sample with 10 mL of H2O and an equilibration of 5 min. After pH determination, that particular sample was made alkali by the addition of 2 M NaOH (2 mL), freeze-dried, and ground to a fine powder using a mortar and pestle. Crude protein in the dried sample was calculated by multiplying the N content, determined using a CN-2000 carbon and nitrogen analyzer (LECO, St. Joseph, MI), by 6.25. Starch content in the dried sample was determined by hydrolyzing the starch during a 2-h digestion with amyloglucosidase and measuring dextrose using a Model 2700 autoanalyzer (Yellow Springs Instrument Company, Yellow Springs, OH). Total polysaccharide was determined using the phenol–sulfuric acid assay described by Daniels et al. (1994), with nonstarch polysaccharide calculated as the difference between total polysaccharide and starch. Loss of a particular substrate was deemed significant if the average substrate contents at the beginning and end of incubation differed (P < 0.05).
Inorganic N 
and urea were extracted from each fresh sample by the addition of 8 mL of 2 M KCl and vigorous shaking for 1 h at room temperature (Mulvaney, 1996). After centrifugation at 10000 x g for 5 min, 2 mL of supernatant was transferred to another tube, acidified by the addition of 5 µL of concentrated H2SO4, and stored at 4°C until analysis. Urea and NH4+ in the extracts were analyzed using a Technicon (Tarrytown, NY) autoanalyzer by a modification of the carbamido–diacetyl reaction and indophenol blue methods, respectively, as previously described (Varel et al., 1999). The Technicon autoanalyzer was also used for NO3– and NO2– analysis by copperized cadmium reduction of NO3– to NO2– followed by NO2– quantification using the Griess–Illosvay reaction (Bundy and Meisinger, 1994).
Headspace gas samples were analyzed for H2, O2, CO, CH4, N2O, and CO2 content using an 8610C gas chromatograph (SRI Instruments, Torrance, CA) equipped with helium ionization and thermal conductivity detectors. The instrument was configured for multiple gas analysis according to the manufacturer's specification using a 10-port gas sampling valve with 1-mL injection loop, a 91-cm-long column (3-mm i.d.) packed with silica gel, and a 91-cm-long column (3-mm i.d.) packed with molecular sieve 5A. Gases were separated using He gas (241 KPa or 35 psi) during a 6.2-min run with the silica gel column experiencing a ramping temperature cycle (initial temperature = 45°C, initial time = 2 min, first temperature ramp = 30°C min–1 to 110°C, second temperature ramp = 10°C min–1 to 120°C, hold at 120°C for 1 min) and the molecular sieve column submerged in an ice water bath. Three gas standard mixes (Scotty Specialty Gases, Plumsteadville, PA) were used nondiluted and diluted in air to produce a range of gas concentrations. Conversion of CH4 and N2O into CO2 equivalents was accomplished by multiplying the respective emissions by 23 and 296 according to a 100-yr time horizon (Intergovernmental Panel on Climate Change, 2001) similar to the method described by Hao et al. (2001)(2004) for feedlot manure compost.
Odorous compounds were extracted from the frozen soil samples by adjusting the H2O content to 3 g H2O per g DM, vigorously shaking the sample for 15 min, and then centrifuging (5 min at 10000 x g). A YSI 2700 analyzer was used to quantify H2O-extractable glucose monomer (a distinct carbohydrate fraction differing from the particulate starch fraction) and L-lactate in the extract using the immobilized glucose oxidase and L-lactate oxidase enzyme methods, respectively. The persistence of free glucose monomer is a good marker for an inactive microbial community. Alcohols (C2 to C6, including isobutanol), VFA (C2 to C8, including isobutyrate, isovalerate, and isocaproate), and aromatic ring containing compounds (phenol, 
-cresol, 4-ethyl phenol, indole, skatole, benzoate, phenylacetate, and phenylpropionate) in the extract were quantified using a Hewlett-Packard 6890 gas chromatograph (Agilent Technologies, Palo Alto, CA) equipped with flame ionization and mass selective detectors. Liquid extract (0.5 mL) was mixed in a 2-mL vial with ethyl butyrate internal standard (1 mM final concentration) and 50 µL of 3 M HCl and crimp-capped, and a 2-µL volume was injected by autoinjector into a split/splitless inlet (5:1 split) operated at 275°C. Compounds were then separated on a 30-m x 0.25-mm-i.d. (0.25-µm phase) HP-INNOWax column (Agilent Technologies) using a ramped flow (initial flow and time = 0.5 mL min–1 for 0.5 min, ramped flow = 20 mL min–2 to 2.5 mL min–1) and ramped temperature (initial temperature and time = 50°C for 1 min, first temperature ramp = 30°C min–1 to 170°C, second temperature ramp = 15°C min–1 to 230°C, third temperature ramp = 5°C min–1 to 235°C, final time = 1 min) program and quantified by flame ionization detector (250°C) relative to standard compound mixes. The identities of the compounds were confirmed by mass spectroscopy as previously described (Miller and Varel, 2001). The content of highly volatile, protonated volatile fatty acids (VFACOOH) was calculated using total VFA concentration, the pH of the sample, and the acid ionization constant for acetic acid (pKa = 4.745) using the following equation:
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All VFA was treated as acetic acid to simplify the calculation. The pKa values for the other dominant VFAs, propionic acid and butyric acid, are 4.873 and 4.81, respectively. This equation was derived from acid ionization (Ka = [H+][A–]/[HA]) and pH (pH = –log[H+]) equations.
The dust potential of each FSM was determined over a range of moisture contents using a two-speed blender (Model 51BL31; Waring Commercial, Torrington, CT), which was modified to produce and collect airborne dust samples as described by Miller and Woodbury (2003). Dust potential measurements were conducted in triplicate according to protocol.
Statistical Analyses
Data were analyzed as a split-plot in time using the general linear model (GLM) procedure of the SAS statistical software package (SAS Institute, 2001). The unit of observation was the pan (n = 54; three replicate pans per manure content x moisture level). The model included effects of manure content (5, 25, and 75%), moisture level, incubation day, pan (manure content x moisture level), manure content x moisture level, manure content x incubation day, and manure content x moisture level x incubation day. Manure content, moisture level, and manure content x moisture level were tested against the mean square of pan (manure content x moisture level). Differences between least-square means were tested with a protected t test. Net accumulation or net loss was determined using two sample paired t tests.
Data for dust potential versus moisture content experiments are presented as the least squares means calculated using the GLM procedure of SAS. For each manure–soil mix sample, data for dust potential versus moisture content were fitted using the NLIN procedure of SAS with the following equation:
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Both constants A and B were determined through an iterative process.
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RESULTS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The pH of the FSM varied with time between moisture and manure levels (Fig. 1) . High moisture content combined with intermediate to high levels of manure content promoted acid accumulation and resulted in the pH decline. In the 25% manure level incubations, pH declined in treatments at 0.67 g H2O g–1 dry FSM and higher. At the 75% manure level, only the 1.50 g H2O g–1 dry FSM moisture level treatment showed a marked decline in pH. Lactic acid, a nonvolatile product of microbial metabolism, accumulated in all of the incubation pans except at the 0.11 g H2O g–1 dry FSM moisture level (all manure levels) and at the 0.25 g H2O g–1 dry FSM moisture level at the 75% manure level (Fig. 1). Lactic acid also accumulated in pans at moderate moisture levels (0.25–1.50, 0.25–0.43, and 0.43–0.67 g H2O g–1 dry FSM moisture levels at the 5, 25, and 75% manure level, respectively), but no pH decline was observed. In the lowest moisture treatments, the pH remained unchanged or increased slightly during the incubation.
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Gas Fluxes and Greenhouse Gas Emissions
Gas fluxes into and out of the incubation pans differed with manure and moisture level (P < 0.001). The hourly consumption of O2 and the emission of CO2 were closely correlated (r = 0.974), and regression analysis for all manure and moisture levels estimated that for every mole of CO2 that was emitted from the FSM, 1.8 mol atmospheric O2 fluxed into the FSM. The largest fluxes were observed at moderate moisture levels (0.25–0.43 g H2O g–1 dry FSM) in the 25% manure level incubation and at higher moisture levels in the 75% manure level incubation. Despite low O2 and CO2 fluxes in the 5% manure level incubation, fluxes of O2 into the FSM were greater (P < 0.05) on Days 2 and 3 in the 0.25 g H2O g–1 dry FSM moisture level pans and on Day 1 in the 0.43 g H2O g–1 dry FSM moisture level pans compared with fluxes on Day 0. Lower O2 and CO2 fluxes were observed at both moisture extremes for all three manure levels.
The highest fluxes of CH4 and N2O were observed at the lower manure levels, 5 and 25% (Fig. 2)
. Methane emissions were only observed on Day 14 and were preceded by H2 emissions (45–735 nmol cm–2 of FSM surface during the 1-h period) on previous sampling days. A brief pulse of N2O was observed on Days 2 and 3 in the 5 and 25% manure level at moisture levels of 
0.43 g H2O g–1 dry FSM. The magnitude of the N2O pulse was inversely related to the manure level. Emission of CO from the FSM was not detected.
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0.25 g H2O g–1 dry FSM. Loss of nonstarch polysaccharide was detected (P < 0.01) at 0.43 to 1.00 g H2O g–1 dry FSM moisture levels in the 75% manure level incubation (Fig. 5). There was also a tendency toward nonstarch polysaccharide loss at the 0.43 g H2O g–1 dry FSM moisture content in the 25% manure level incubation. Interestingly, the growth of coliforms, generic E. coli, and E. coli O157:H7 was also associated with these particular treatments where nonstarch polysaccharide may have been consumed (Berry and Miller, 2005). All other moisture and manure level combinations remained unchanged in nonstarch polysaccharide (P > 0.535).
Nitrogen Retention
Significant (P < 0.05) losses of total KCl-extractable N, the sum of urea, NH4+, NO3– and NO2– contents, were observed at 0.25 to 0.43 g H2O g–1 dry FSM in the 25% manure incubations and at moisture contents of 0.25 g H2O g–1 dry FSM in the 75% manure incubations (Fig. 6)
. The pooled standard error, however, was very large (25.3 µmol g–1 dry FSM) due to the large variation in initial urea concentrations at the 25% manure level incubation. Thus, our ability to determine the significance of extractable N losses from low-N pans was compromised. A two-sample paired t test was used to assess whether extractable N content decreased from Day 0 to Day 14 for each manure and moisture level combination. Extractable N content decreased (P < 0.05) in all manure and moisture contents except at the 0.11 g H2O g–1 dry FSM moisture level in the 25 and 75% manure level incubations (P 0.125), where urea, NH4+, and NO3– and NO2– persisted. In addition, the NO3– and NO2– component of KCl-extractable N also persisted in the 0.25 g H2O g–1 dry FSM in the 5 and 75% manure incubations. Nitrate and nitrite increased (P < 0.04) in two of those incubations by 5.3 µmol N g–1 dry FSM in the 0.25 g H2O g–1 dry FSM (5% manure incubation) and 1.0 µmol N g–1 dry FSM in the 0.11 g H2O g–1 dry FSM (25% manure incubation).
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DISCUSSION |
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An indirect effect of moisture was its impact on microbial metabolism, a well-established principal in soil microbiology (Parr et al., 1981). Three classes of metabolism (inactive, aerobic, and fermentative) can be inferred based on the accumulation, loss, or persistence of key microbial substrates and metabolites in the artificial feedlot soils (Table 1). Temporary lactic acid accumulation, persistence of glucose monomer, and declining KCl-extractable N and starch contents provided evidence for the general metabolic state (active vs. inactive) of the microorganisms except in the most extreme case of lowest manure (5%) and moisture contents (0.11 g H2O g–1 of FSM) where the consumption of glucose monomer was very slow and lactic acid, starch content, and extractable N did not change. Declining N contents, as measured in these experiments, could be attributed to one or a combination of several processes, including NH3 volatilization, microbial N immobilization, or nitrification–denitrification, that require a metabolically active microbial community or functional urease enzymes in the case of NH3 volatilization (Hutchinson et al., 1982). Starch losses associated with the "active" metabolic state were consistent with earlier cattle feedlot manure slurry studies (Miller and Varel, 2001, 2002) identifying starch as the primary substrate, rather than protein, which is a major microbial substrate in swine manure slurry incubations (Miller and Varel, 2003). Thus, the link between fecal starch content and odors originally proposed by Watts and Tucker (1993) is supported by our results.
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Aerobic consumption of VFA, increased emission of VFA, or a combination of both could account for the loss of VFA in the aerobic incubations.
Manure content affected FSM processes in two ways. First, the manure modulated the effect of moisture by requiring more moisture at higher manure contents to elicit conditions observed at lower manure contents. The relationship between dust potential, MT, and OM content most directly illustrated this point. With increasing manure content, more moisture was required to reduce dust potential. A similar pattern was observed in the pan incubations in the subtle shift of a variety of processes including glucose consumption, lactic acid production, O2 and CO2 fluxes, pH declines, nitrogen persistence, and starch consumption (Table 1). The likely mechanism involves a shift in the soil water matric potential, which is closely tied to the potential for microbial activity (Paul and Clark, 1996). Increasing manure content likely decreased the availability of water for microbial processes.
A second consequence of varying manure content was that increasing manure content modulated microbial processes by increasing the availability of microbial substrate and the subsequent buildup of inhibitory metabolic byproducts. The production of various GHGs exemplifies this point. At low manure contents under fermentative conditions, pH declined minimally, CH4 was produced, and N2O was the dominant GHG emission during the incubation period. The source of N2O is likely attributable to either microbial denitrification or nitrification, both of which have been observed in feedlot surface soils (Woodbury et al., 2001). Incubation conditions likely favored a denitrifying mechanism because N2O efflux was observed only at the highest moisture levels (fermentative) and not at moisture contents of <0.43 g H2O g–1 dry FSM (aerobic conditions of high O2 and CO2 fluxes) that should have favored nitrification activity. The absence of N2O emissions at the 75% manure level may reflect a robust demand by denitrifying bacteria for any electron acceptor including N2O. Brown et al. (2000) attributed the lack of N2O emission during the first week of dairy manure incubation to a similar mechanism; when the aerated crust of their manure incubations was inadvertently mixed into the anaerobic center of the incubation, any NO3– or N2O produced in the crust was scavenged by denitrifying bacteria. Methane emissions were expected at highest moisture and manure contents, but the pH decline was large enough to inhibit methanogenesis, and CO2 was the dominant GHG emission. Emissions of CH4 and N2O from feedlot manure compost (Hao et al., 2001, 2004) differed with our results, but are likely related to anaerobic conditions in the center of the compost pile. However, both FSM and compost emissions were dominated by non-CO2 GHG (CH4 and N2O).
Increasing the manure content also affected N transformations in the FSM. Ammonia volatilization is a major route of N loss from cattle feedlots, with urea hydrolysis to NH4+ and its subsequent volatilization under conditions of alkaline pH a primary NH3 emission source (Hutchinson et al., 1982). McGinn et al. (2002) measured initially large fluxes of NH3 followed by much smaller fluxes from a high-moisture (1.7 g H2O g–1 DM) mixture of cattle manure and bedding. Although NH3 volatilization was not directly measured in our incubations, declining extractable N in the metabolically active incubations was consistent with the results of McGinn et al. (2002). However, a coupled nitrification–denitrification mechanism resulting in N2 emission cannot be rejected. An alternative beneficial sink for NH4+ is N immobilization, which would increase the fertilizer value of the manure. Adams et al. (2004) found that incorporating more OM into feedlot surfaces decreased N losses from feedlot pens, presumably through increased N immobilization. Under aerobic conditions in high manure content incubations, there was clear evidence for N incorporation into the FSM OM fraction. Further research optimizing N immobilization in feedlot surface material relative to NH3 emission will have significant impact and should be pursued.
As a source for potential odor compound production, manure-rich feedlot surfaces are undoubtedly much more troublesome than low manure surfaces. Volatile fatty acids are major end products produced during the anaerobic decomposition of cattle feedlot manure and arise primarily from incomplete starch decomposition (Miller and Varel, 2001, 2002). These compounds have also been linked to the malodor downwind from confined animal feeding operations (McGinn et al., 2003; Williams, 1984; Zhan et al., 2001). We expected VFA to increase in the fermentative incubations at higher manure contents, but the VFA content did not increase appreciably. Instead, the fraction of VFACOOH increased due to declining FSM pH. Thus, the effect of decreasing pH on the forms of VFA may account for increased odor emission after rainfall events more so than an increase in VFA content alone (Watts et al., 1994).
Predominant Environmental Issues
Understanding the various consequences of the varying manure and moisture landscape and its impact on microbial activities provided useful insights into potential pen management avenues. The dominant microbial metabolisms aligned well with particular environmental concerns and emphasized the role of microorganisms in the feedlot environment (Fig. 8)
. Microbial inactivity and dust emission issues were related; low water availability inhibited microbial metabolism and urea hydrolysis, but also enhanced the formation of dust particles. Under conditions favoring aerobic metabolic activity, KCl-extractable N loss at low manure contents and CO2 emission in high manure contents were the primary concerns. In an accompanying paper, pathogen persistence and in some cases pathogen growth was observed in aerobic treatments at higher moisture contents (Berry and Miller, 2005). Incorporating those observations into the conceptual model showed that not all aerobic activity was beneficial. Finally, fermentative conditions produced a variety of effects. The loss of KCl-extractable N was indicated at all manure levels under fermentative conditions, whereas there was a transition from GHG production to enhanced odor emission resulting from acid accumulation.
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The results of these laboratory experiments illustrate that a wide range of environmental consequences are possible based on initial manure and moisture conditions. Thus, the conclusions drawn from laboratory incubations evaluating only a single set of moisture and manure conditions will not be applicable to the range of manure and moisture contents observed in cattle feedlot pens. The results of these experiments may be biased to some degree because the steps necessary to prepare dried manure (oven drying and grinding) and the incubation conditions (short incubation period, FSM depth and bulk density, sunlight effects, temperature variation) differ in subtle ways from feedlot conditions. However, as an initial investigation, the results of this study provide insights into the underlying microbial processes and relationships between emissions of concern and directions for future investigations.
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CONCLUSIONS |
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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