M b chiavegato, j e rowntree, d carmichael, and w j powers 2015 enteric methane from lactating beef

Published March 31, 2015

Enteric methane from lactating beef cows managed with high- and low-input grazing systems1 M. B. Chiavegato, J. E. Rowntree, D. Carmichael, and W. J. Powers2 Department of Animal Science, Michigan State University, East Lansing 48824

ABSTRACT: The objective of this study was to compare methane (CH4) emissions from lactating beef cows grazed with different combinations of stocking rate and density. We hypothesized that a low stocking rate coupled with high-stocking-density grazing management would result in poorer forage quality, thereby increasing enteric CH4 emissions. System A (SysA) consisted of 120 cow-calf pairs rotating on a total of 120 ha divided into 2-ha pastures (stocking rate 1 cow/ha, stocking density 112,000 kg BW/ ha, rest period of 60 to 90 d). System B (SysB) consisted of 16 groups of 4 cow-calf pairs each rotating on a 1.6-ha pasture (stocking rate 2.5 cows/ha, stocking density 32,000 kg BW/ha, rest period of 18 to 30 d). Enteric CH4 measurements were collected using a sulfur hexafluoride (SF6) tracer gas method.

Sampling occurred during 2012 and 2013 in 2 periods: the beginning (P1) and end of the grazing season (P2). Cannulated Angus cows were stratified by weight, age, and parity and were assigned to each treatment (n = 6) in a crossover design with a doubly repeated measures design, with period and day as repeated measures (α = 0.05). Dry matter intake was determined using chromic oxide (Cr2O3) as a marker.

Forage samples were collected (n = 3) for nutrient composition analyses and total forage mass determi-

nation. Forage botanical composition was determined using the dry-weight-rank method. Postgrazing herbage mass was greater for SysA during P2 in 2012 (P < 0.01) and 2013 (P = 0.01). Grasses were predominant and represented 67% to 96% of pastures; legumes contributed 3% to 21% of pastures across periods and treatments. The proportion of legumes tended to be higher in SysB pasture sites in P2 than in P1. There were no treatment effects on DMI. There was a period effect on DMI (P < 0.01); DMI of SysA and SysB cows increased from P1 to P2 (4 and 1.1 kg DMI/d increase, respectively). Cows ingested, on average, 2.6% (SysA) and 2.8% (SysB) of their BW. There was no year effect on CH4 emissions (P = 0.16). Daily enteric CH4 emissions did not vary with treatment and ranged from 195 to 249 g CH4/d across treatment. Enteric CH4 emissions per unit GE intake varied with treatment during P1 (6.4% and 3.8% for SysA and SysB, respectively; P < 0.01). Across treatments and periods, enteric CH4 emission per unit GE intake was 4.6%, which could be considered low for grazing lactating beef cows. It is likely that cows in the present study were selecting high-quality forage and produced comparatively lower CH4 emissions.

Key words: beef cattle, enteric methane, grazing management, sulfur hexafluoride © 2015 American Society of Animal Science. All rights reserved. J. Anim. Sci. 2015.93:1365–1375

doi:10.2527/jas2014-8128 INTRODUCTION 1The

authors thank the Michigan State University Animal Agriculture Initiative for financial support of this project. In addition, the authors thank Natalie Palumbo, Brooke Latack, Katelyn Thompson, Rachel Baumgardner, Mark Schilling, and Jolene Roth for support during sampling and sample analyses (Michigan State University).

2Corresponding author: wpowers@msu.edu Received May 30, 2014.

Accepted November 18, 2014.

The production of methane (CH4) by cattle has become the subject of scientific debate as the concern over climate change increases. To understand the C flux in grazing systems, quantifying and understanding the impact of management on enteric CH4 production is warranted. The primary factors

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affecting enteric CH4 production are the quantity and quality of the diet. As forage fiber content increases, nutrient digestion and passage rate decrease, increasing the predominate ruminal fermentation pathways and subsequent enteric CH4 production. Therefore, implementing management tools at the farm level that increase pasture quality potentially decreases enteric CH4 emissions from grazing cows (Beauchemin et al., 2008). Grazing management is a combination of several factors, such as stocking rate, density, and rest periods. These factors define the relationship between herbage supply (Animut et al., 2005) and forage quality, thus influencing herbage utilization efficiency, animal performance, and production per hectare (Pinares-Patino et al., 2007). The majority of studies measuring CH4 emissions from the beef industry focus on steers or heifers (Ricci et al., 2013). Previous research has measured emissions from lactating beef cows fed mixed alfalfa-concentrate diets (Reynolds and Tyrrell, 2000) or grazing alfalfa-grass and grass-only pastures (McCaughey et al., 1999). The influence of management decisions in the context of systems on CH4 emissions from beef cows is missing. The objective of this study was to compare CH4 emissions from lactating beef cows grazed on different combinations of stocking rate and density within the confines of a low-input vs. management-intensive grazing criterion. We hypothesized that low stocking rate coupled with highstocking-density grazing management (low input or extensive system) would result in poorer forage quality because of longer rest periods, thereby increasing enteric CH4 emissions from lactating beef cows.

MATERIAL AND METHODS All animal procedures were approved by the Michigan State University Animal Care and Use Committee (protocol 04/12-078-00).

Pasture Management This study was conducted at the Michigan State University Lake City AgBioResearch Center (LCRC; latitude: 44°18′N, longitude: 85°11′W; elevation: 377 m) located in northwest Michigan. The region, developed over glaciated soils, was primarily deciduous and coniferous forest before farming entered the area in the 1860s. Sixty-four percent of the study area is dominated by the Nester soil series, composed of a well-drained sandy loam containing 1% to 6% slopes.

The remaining area is dominated by the Kawkawlin soil series, characterized by heavier soil texture and gentle slope (Natural Resources Conservation Service,

1999). Additional climatic and soils information is provided by Chiavegato et al. (2015).

Beginning in 2010, 2 management practices have been implemented at LCRC. The first is a low-input system utilizing moderate stocking rates and high stocking density (SysA). In this system, cattle are often moved up to 3 times daily. The purpose of this system is to monitor the impact of stock density on ecological variables such as soil OM, water infiltration, and total soil nitrogen. Cattle grazing under high density hypothetically increase the trample:graze ratio, thereby increasing litter layer and more evenly depositing urine and manure across the landscape. At the farm level SysA consisted of 120 cow-calf pairs rotating on a total of 120 ha, divided into 2-ha pastures with three 0.70-ha paddocks each. Cow-calf pairs were moved to a new paddock 3 times daily (at approximately 0800, 1200, and 1600 h). The equivalent stocking rate was 1 cow/ha, and the stocking density was approximately 112,000 kg BW/ha. The rest period varied from 60 to 90 d during the course of the growing season depending on plant growth. Cow-calf pairs grazed each paddocks 2 to 3 times per year. In the second system (SysB) the alternative management practice is more oriented to animal performance vs. landscape improvement.

The second system, SysB, employed a high stocking rate and comparatively lower stocking density on 26 ha divided into 1.6-ha blocks (16 blocks or pastures total). Each day, the management allocated 1 paddock, approximately 0.08 ha, to be grazed by 4 cow-calf pairs (moved at approximately 0800 h). Therefore, 4 cow-calf pairs resided in each pasture, for a total of 64 cow-calf pairs; the equivalent stocking rate was 2.5 cows/ha, and the stocking density was 32,700 kg BW/ ha. The equivalent stocking rate was 2.5 cows/ha, and the stocking density was 32,000 kg BW/ha. The goal of this system was to increase animal performance by more aggressive defoliation and shorter rest periods to encourage higher forage quality compared with that of SysA. This system is equipped with a K-Line Irrigation system (St. Joseph, MI), a low-pressure pod-style irrigation with a 10-d return if needed. Four cow-calf pairs in each of 16 paddocks all within a common pasture were moved to a new paddock once daily (at approximately 0800 h). The rest period varied from 18 to 30 d during the course of the growing season depending on plant growth. Cow-calf pairs grazed each paddock 4 to 5 times per year. The pasture areas in SysB were irrigated as needed, whereas no irrigation was applied to SysA pasture sites. No herbicides or fertilizers were added to either treatment during the project years. At the time of enteric CH4 measurements, all SysA animals, including but not limited to the subset

Enteric methane from lactating beef cows

of animals for which enteric CH4 measures were collected as described below, were placed in paddocks closer to the head gate area, adjacent to SysB pasture, to facilitate transportation of cow-calf pairs to the head gate area for sample collection. Although adjacent to SysB, the paddocks were maintained in a way that mimicked the SysA system across the research center.

A portion of the grazed cows was selected for enteric CH4 measures (n = 12). Angus cows were stratified by weight, age, and parity and were assigned to each treatment (SysA and SysB; n = 6) for the first period within each sample year. For the second period in each year, treatment assignments were reversed to allow a crossover design. To achieve the desired grazing density, additional cows were added to each herd as needed but were not included in enteric CH4 sampling.

During P1 of 2012, hematomas developed in a subset of cows, and after consultation with the attending veterinarian, we attributed the swelling to head gate pressure used while collecting samples. Before P2 of the 2012 grazing season, 2 cows were removed from the sampling because of the swelling. Therefore, P2 was performed with 10 cows (5 replicates per treatment). Because the 2 cows removed were assigned to the same treatment, cows were reassigned to treatments in advance of P2 such that weight, age, and parity were similar between treatments. During the 2013 sample periods 6 replicates per treatment were available.

Enteric Methane Measurements Enteric CH4 measurements were collected from respired air of grazed cows 2 times during the grazing season using sulfur hexafluoride (SF6) as a tracer gas (Johnson and Johnson, 1995). Sampling occurred during the grazing seasons of 2012 and 2013 over two 7-d periods with the following dates: from June 4 to 10 (period 1, P1) and from September 4 to 11 (period 2, P2) in 2012 and from June 5 to 10 (P1) and from September 11 to 17 (P2) in 2013. Precalibrated permeation tubes containing SF6 were placed into the reticulorumen of each animal via bolus in 2012. Cows were cannulated in January 2013 so that we could retrieve permeation tubes at the end of the grazing season and confirm that SF6 release rate was comparable to the rate measured before initial placement in the spring.

Precalibrated permeation tubes containing SF6 were placed into the reticulorumen of each animal through the cannula in 2013. Expired gases were collected with a sampling apparatus containing a collection canister (PVC) and modified halter. The permeation tubes, canisters, and halters were built following the protocol described by Johnson et al. (2007).

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Canisters were removed daily at 0830 and 1530 h, evacuated, and replaced after the contents were sampled. Cows were moved to a chute area for each canister evacuation, and total time to sample and replace canisters for all animals in both treatment groups was approximately 1 h each sampling. To collect enteric CH4 and SF6 samples, the canisters were vacuumed to approximately −2.7 kPa with a vacuum pump (2 stages, Robinair, Owatonna, MN). After the collection period, canisters were connected to a dilution system, and the final pressure was recorded. Nitrogen was then added slowly until canister pressure reached 117.2 kPa. Pressure readings were recorded to calculate the dilution factor (Johnson et al., 2007). Pressure was measured with a GE Druck DPI 705 digital pressure indicator (combined nonlinearity, hysteresis, and repeatability of ±0.1% full scale, maximum torque of 2.259 N·m; GE Druck, South Burlington, VT). After pressurization to 117.2 kPa, the contents of the canisters were transferred under positive pressure to evacuated vials.

Vials were pressurized at approximately −27.6 kPa (maximum flow: 0.05 m3/min; maximum pressure: 413.7 kPa; maximum vacuum: 15,000 kPa; GAST, Benton Harbor, MI). Vial pressure was checked with a Media Gauge series digital pressure gauge (ultralowpressure range of ≤103.4 kPa, 0.25% full-scale accuracy; SSI Technologies Inc., Janesville, WI). Sample vials were stored at room temperature and transported to a Michigan State University laboratory for analysis by gas chromatograph (GC).

The GC (Shimadzu GC-2014) was equipped with electron capture (ECD) and flame ionization detectors (FID; Shimadzu, Addison, IL). Carrier gas was ultrapure nitrogen gas, with total flow of 40 mL/min and purge flow of 0.5 mL/min. The column oven was maintained at 75°C, FID was maintained at 250°C, and ECD was maintained at 325°C. The GC was equipped with a headspace automatic sampler (COMBI Pal, LEAP Technologies, Carrboro, NC). Calibration curves contained at least 5 points and were generated for CH4 with standard gas of 20.42 mg/kg CH4 and for SF6 with pure SF6 gas. Standard gas was diluted with atmospheric air in vials to generate the curve, which also included a 0 point (atmospheric air). The GC was calibrated just before analyses and once every 2 wk.

To adjust for background CH4, air samples were collected at 2 points upwind of each grazed paddock, using the canister method (Johnson and Johnson, 1995).

Canisters were placed on fences adjacent to grazed paddocks. Background canisters were sampled following cow sampling, twice daily for 7 d (first collection at 0930 h and the second collection at 1630 h daily).

Daily CH4 emissions and the CH4:SF6 ratio of concentrations in breath samples were calculated after

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adjusting for background gas concentrations (Johnson and Johnson, 1995). Methane emission rate was calculated from collected SF6 and CH4 concentrations and from SF6 permeation tube release rate according to the following equation: QCH4 = QSF6 × ([CH4]cows − [CH4]background)/[SF6],

[1]

where QCH4 is the CH4 emission rate (g/min), QSF6 is the permeation tube SF6 release rate (g/min), [CH4] cows is the CH4 concentration quantified from cows (µg/m3), [CH4]background is the CH4 concentration quantified from background canisters (µg/m3), and [SF6] is the SF6 concentration in samples (µg/m3).

Intake Determination and Forage Analyses To determine intake, chromic oxide (Cr2O3) was used as a marker. Fecal output was determined from the passage kinetics of Cr2O3 (Fenton and Fenton, 1979). A dosage of 6 g of Cr2O3 was administered to the animals twice daily for 7 d via oral bolus in 2012 and cannula in 2013. Fecal samples were collected twice daily during the last 4 d of the dosage period. Feces were dried at 65°C until constant weight. Dried feces were ground with a Wiley mill (carbon steel, 4 adjustable hard tool steel knives; Thomas Scientific, Swedesboro, NJ) and were analyzed by atomic absorption spectrophotometry.

The atomic absorption spectrophotometer was equipped with a 357.9-nm wavelength lamp and air-acetylene flame (PerkinElmer, Waltham, MA). Fecal Cr concentrations were used to estimate fecal output according to Fecal output, g/d = m arker consumed, g/d/marker concentration in feces, g/g DM, [2] and DMI was calculated on the basis of the following equation: DMI, kg/d = fecal output, g/d × 100/indigestibility of DM, %.

[3] Indigestibility of DM was determined on the basis of diet apparent digestibility. Cows were weighed at the beginning (d 1) and at the end (d 7) of each sampling period.

Pre- and postgrazing forage samples were collected twice during the 7-d period, on d 2 and 6. Within each 1.6-ha pasture, 3 forage sampling sites (approximately 0.5 ha) were designated as forage sampling areas. Each sampling area was split equally into 2 zones (west and east). The day before grazing, the pregrazing pasture

biomass was sampled within each zone by clipping 3 randomly placed 0.25 m2 quadrats to a 5-cm stubble height with Gardena 8803 battery-operated harvest shears (Gardena, Ulm, Germany). Samples were composited by sampling zone and weighed, and an average wet weight from each zone was recorded. After mean wet weights were recorded, forage samples from both zones were combined and thoroughly mixed, and a 200-g subsample was collected. Subsamples were oven-dried at 60°C for 48 h, weighed, and ground in a Wiley mill. The same sampling process was repeated after cattle grazed the paddock to determine the postgrazing (residual) biomass. The pregrazing and postgrazing samples were sent to DairyOne (Ithaca, NY) for near infrared analysis. The parameters analyzed were CP, ADF, NDF, lignin concentration, and in vitro total digestibility (IVTD). Forage GE, digestible NDF (DNDF), and apparent digestibility (AD) were determined on the basis of calculations proposed in the nutrient requirements of cattle protocol (NRC, 2001): AD = 100 − [(100 − DNDF) × (NDF/100)]. [4] Before pregrazing forage sample collection, the botanical composition of the paddocks was assessed using the dry-weight-rank method (Mannetje and Haydock, 1963). Six 0.25-m2 quadrats were randomly placed in each paddock, and the plants species within the quadrats were identified.

Statistical Analysis For the first period in each year cow-calf pairs were randomly assigned to treatments. For the subsequent periods, a crossover design was implemented in a doubly repeated measures design, with period and day as repeated measures. The variance-covariance matrix structure chosen was unbalanced autoregressive to account for the doubly repeated measures. The model was as follows:

y = µ + ρj + τl + γk + λm + τγlk + τλlm + ejlkmi, where µ is the overall mean, ρj is the random effect of the jth cow (ρj ~ N(0, σ2ρ)), τl is the fixed effect of the lth treatment, γk is the fixed effect of the kth year, λm is the fixed effect of the mth period, τγlk is the interaction between the lth treatment and the kth year, τλlm is the interaction between the lth treatment and the mth period, and ejlkmi is the residual term (ejlkmi ~ N(0, Σ)).

Pasture-related variable samples were collected and analyzed with a completely randomized design (with

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paddock as the experimental unit, n = 3), where paddock was the random term and the compressed term year × period was the repeated measure. Statistical analyses were performed using SAS software (version 9.2; SAS Inst. Inc., Cary, NC). All tests were performed with 95% confidence (α = 0.05).

RESULTS AND DISCUSSION Herbage Mass, Forage Composition, and Forage Nutritional Characteristics Because there was an effect of year (P < 0.01) for some measures, means are shown separately for 2012 and 2013 (Table 1). Pregrazed forage growth was not affected by treatments (Table 1). An interaction of treatment and period could be expected for herbage mass pregrazing because of longer rest periods for SysA (60 to 90 d compared with 18 to 30 d for SysB pasture sites) that might contribute to greater forage mass being available in P2. Irrigation (as needed) and frequent defoliation on SysB pasture sites might have increased the forage production at these sites, explaining the lack of difference. Cow-calf pairs grazed SysB pasture sites 4 to 5 times per year, whereas SysA pasture sites were grazed 2 to 3 times per year. Furthermore, the greater frequency of grazing applied to SysB pasture sites might have stimulated forage growth. Additionally, SysB had a greater amount of legumes during P2 than SysA (Table 2) and likely had a greater amount of N available to plants, which might also have contributed to forage growth.

Postgrazing herbage mass was greater for SysA during P2 in both years, resulting in a significant interaction between treatment and period (Table 1). The SysA pasture sites were given longer rest periods, and forage offered to cows was reproductive and mature. Herbage disappearance might not be an accurate predictor of intake; however, observed herbage disappearance was in agreement with the DMI estimated using Cr2O3 as a marker. We believe that cows grazed selectively, trampling down a great amount of forage mass, increasing postgrazing herbage mass.

The pastures sites were composed of mixed grass and legume species. The 3 most predominant species observed in each pasture site are given in Table 2.

Grasses were predominant and represented from 67% to 96% of the pastures across periods and treatments.

However, legumes were always found, contributing from 3% to 21% of the pastures across periods and treatments (Table 2). Frequently, more than 1 species of legume was present (data not shown).

The same species were present throughout the year but in different proportions. In SysA pasture sites or-

Table 1. Pre- and postgrazing forage mass and forage disappearance for pastures grazed with different grazing management strategies Period2 System1

1

Effect (P-value) 2

Treatment SEM Treatment Period × period

2012 grazing season Pregrazing, kg/ha SysA 2,278 SysB 2,670 Postgrazing, kg/ha SysA 1,375 SysB 1,978 Disappearance, % SysA 38 SysB 32 2013 grazing season Pregrazing, kg/ha SysA 3,851 SysB 4240 Postgrazing, kg/ha SysA 2,776 SysB 2,963 Disappearance, % SysA 26 SysB 30

127

0.74

<0.01

0.06

121

0.15

0.10

<0.01

6

0.09

0.30

0.23

157

0.83

0.05

0.14

82

0.06

<0.01

<0.01

5

0.28

0.03

0.76

3,824* 3,508* 2,580a,* 1,454b 26a 52b

3,735 3,443 2,167a,* 1,524b,* 42* 55

a,bMeans

within a column with different superscripts differ (P < 0.05). *Means within a row with different superscripts differ (P < 0.05).

1SysA: 1 cow/ha stocking rate and 112,000 kg BW/ha stocking density; SysB: 2.5 cows/ha stocking rate and 32,000 kg BW/ha stocking density.

2June 4 to 10, 2012 (period 1), September 4 to 11, 2012 (period 2); June 5 to 10, 2013 (period 1), September 11 to 17, 2013 (period 2).

chard grass (Dactylis glomerata) and bromegrass (Bromus inermis) were the predominant grasses, and bird’sfoot trefoil (Lotus corniculatus) was the predominant legume, but red (Trifolium pratense) and white clover (T. repens) were also observed (Table 2). In SysB pasture sites, Kentucky (Poa pratensis) and orchard grasses were the predominant grasses over the 2 yr of study (Table 2). White clover was the most predominant legume during P1, and red clover was predominant during P2. The grass-legume ratio of pasture sites varied from 96:4 (SysA pasture sites during P2 of 2012) to 69:4 (SysB pasture sites during P2 of 2013). Our results are in agreement with previous research that found few species responsible for a large proportion of the DM production in mixed grass and legume pastures (Sanderson et al., 2005; Skinner et al., 2006).

Previous studies indicated that grazing intensity did not affect species composition (Kruess and Tscharntke, 2002; Dumont et al., 2009; Ren et al., 2012). The proportion of legumes tended to be higher in SysB pasture sites from P1 to P2 (not analyzed statistically). The SysA pasture sites were given lon-

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Table 2. Forage composition of pastures grazed with different grazing management strategies Period 12 System1

Forage

2012 grazing season SysA

SysB

2013 grazing season SysA

SysB

1SysA: 2June

Period 22 Percentage of total composition

Forage

Percentage of total composition

Orchard (Dactylis glomerata) Birdâ&#x20AC;&#x2122;s-foot trefoil (Lotus corniculatus) Dandelion (Taraxacum officinale) Kentucky (Poa pratensis) Orchard (Dactylis glomerata) White clover (Trifolium repens)

70 17 13 34 54 7

Bromegrass (Bromus inermis) Orchard (Dactylis glomerata) Red clover (Trifolium pratense) Timothy (Phleum pratense) Red clover (Trifolium pratense) Bromegrass (Bromus inermis)

85 11 4 70 15 11

Kentucky (Poa pratensis) Orchard (Dactylis glomerata) Red/white clover (T. pratense/repens) Kentucky (Poa pratensis) Orchard (Dactylis glomerata) Red/white clover (T. pratense/repens)

50 17 7 54 30 3

Bromegrass (Bromus inermis) Orchard (Dactylis glomerata) Birdâ&#x20AC;&#x2122;s-foot trefoil (Lotus corniculatus) Orchard (Dactylis glomerata) Kentucky (Poa pratensis) Red clover (Trifolium pratense)

51 26 9 55 14 21

1 cow/ha stocking rate and 112,000 kg BW/ha stocking density; SysB: 2.5 cows/ha stocking rate and 32,000 kg BW/ha stocking density.

4 to 10, 2012 (period 1), September 4 to 11, 2012 (period 2); June 5 to 10, 2013 (period 1), September 11 to 17, 2013 (period 2).

ger rest periods; consequently, at the end of the grazing season (P2) the plants were tall and reproductive.

Shorter grazing returns result in frequent defoliation, which keeps grasses at shorter heights. Shorter grasses have less shading effect, allowing the development of plants with different growing habits, such as legumes (Wong and Wilson, 1980; Groya and Sheaffer, 1981).

Alfalfa (Medicago sativa) has an upright growth habit, red clover and birdâ&#x20AC;&#x2122;s-foot trefoil have an upright to decumbent growth habit, and white clover has a prostrate growth habit (Hannaway and Cool, 2004). Indeed, we observed the predominance of red and white clover on SysB pasture sites, which with more frequent defoliation had shorted grasses, allowing the development of prostrate growth habit legumes (Table 2).

The SysA grass height during P2 may have shaded and contributed to some impairment in the development of legumes, thus decreasing their proportion. Another factor that might have contributed to the decreased abundance of legumes during P2 in SysA pasture sites was dry weather during the summer (end of the season; P2). The SysB pastures areas were irrigated as needed (on average, 19.6 cm of water applied to each 1.6-ha paddock grazed during the 2012 and 2013 grazing seasons), which might have allowed the growth of legumes throughout the year.

Botanical shift affects forage quality, complicating the estimation of the nutritional value of a pasture (Belesky et al., 1999; Skinner et al., 2004). Dumont et al. (2009) suggested that the distribution evenness of plant species is greater under high stocking rates. To decrease the error associated with forage quality esti-

mation of forage quality, we collected 3 replicates of composited random samples per treatment. However, animals graze selectively, and the forage quality results may not accurately represent the DM consumed by cow-calf pairs. Grazing animals select their feed and prefer living to dead material, younger to older material, leaf to stem, and legume to grass leaves (Hodgson, 1990; Popp et al., 1997).

Diet nutrient concentrations shown in Table 3 represent pregrazed forage samples clipped from pastures the day before grazing. However, these characteristics might not accurately represent the diet consumed by animals because of selective grazing. The nutritional characteristics of the pastures differed by year (P < 0.01); therefore, means are presented separately for the 2012 and 2013 grazing seasons (Table 3). Each year, an interaction was observed for CP, IVTD, and GE content. During P1 forage characteristics were quite similar between treatment pasture sites. As the grazing season proceeded, the impact of grazing management increased, mostly because of longer rest periods given to SysA pasture sites. Period and treatment effects were observed for forage nutritional characteristics.

Crude protein content increased in SysB pasture sites from P1 to P2 (12.3% and 15.5% for P1 and P2, respectively; P = 0.01) during 2012 but remained constant from P1 to P2 (13.2% and 11.4% for P1 and P2, respectively; P = 0.09) during 2013. In SysA pasture sites, CP decreased considerably from P1 to P2 during 2013 (13.2% and 8.1% for P1 and P2, respectively; P < 0.01).

During both years, NDF content increased from P1 to P2 in SysA pasture sites (64% to 70% for P1 and P2

Enteric methane from lactating beef cows

Table 3. Nutritional characteristics of pastures grazed with different grazing management strategies Period2 System1

1

Effect (P-value) 2

2012 grazing season CP,3 % SysA SysB NDF, % SysA SysB Lignin, % SysA SysB IVTD,4 % SysA SysB GE, MJ/kg SysA SysB

11.7 12.3

11.4a 15.5b,*

65.6 65.1

73.9a,* 64.1b

3.9 3.1

6.1a,* 4.8b,*

78.1 80.3

66.7a,* 78.9b

18.8 19.5

17.6 17.6

2013 grazing season CP, % SysA 13.2

8.1a,*

SysB NDF, % SysA SysB Lignin, % SysA SysB IVTD, % SysA SysB GE, MJ/kg SysA SysB

13.2

11.4b

62.5 60.2

66.8a,* 62.1b

3.3 3.1

6.6* 6.4*

82.5 83.9

58.4a,* 65.1b,*

17.6 17.6

17.4 17.3

Treatment Treatment Year Period × period <0.01

—

0.08

<0.01

<0.01

—

0.32

<0.01

<0.01

—

<0.01

<0.01

<0.01

—

<0.01

<0.01

0.64

—

0.02

0.44

<0.01

—

<0.01

<0.01

<0.01

—

0.04

0.25

0.27

—

<0.01

0.83

<0.01

—

<0.01

< 0.01

0.35

<0.01 <0.01

0.05

a,bMeans

within a column with different superscripts differ (P < 0.05). *Means within a row with different superscripts differ (P < 0.05).

1SysA: 1 cow/ha stocking rate and 112,000 kg BW/ha stocking density; SysB: 2.5 cows/ha stocking rate and 32,000 kg BW/ha stocking density.

2June 4 to 10, 2012 (period 1), September 4 to 11, 2012 (period 2); June 5 to 10, 2013 (period 1), September 11 to 17, 2013 (period 2).

3In vitro total digestibility.

across years, respectively; P < 0.01) but remained constant in SysB pasture sites (on average, 63%; Table 3).

Similarly, IVTD decreased in SysA pasture sites from P1 to P2 during both years (79% and 60% for P1 and P2 across years, respectively; P < 0.01). The changes in CP, NDF, and IVTD contents suggested a poorer forage quality in SysA pasture sites during P2 when compared with that of SysB pasture sites (Table 3).

The larger differences observed (between SysA and SysB pasture sites) during P2 were expected. On the basis of diet composition analyses it is possible to

1371

infer that at the end of the grazing season (P2), SysA pasture sites were composed of mature forages in the reproductive stage. The SysB pastures sites, managed with shorter rest periods, maintained young vegetative forage throughout the season. Irrigation and frequent defoliation might have contributed to the higher forage quality of SysB pasture sites during P2, allowing the development of legumes during P2 (Table 2).

Animal Intake Cows were maintained in a single herd for most of the year and were divided into SysA and SysB herds (for diet adaptation) approximately 14 d before enteric methane samples were collected. Therefore, treatment effects on BW are not meaningful. Body weights increased from 2012 to 2013 (P < 0.01) and from P1 to P2 (Table 4).

A treatment by period interaction was observed each year for DMI, GE intake (GEI), NDF intake (NDFI), and DNDF intake (DNDFI). Year effects were observed for most variables (Table 4). Treatment effects on DMI and DNDFI (Table 4) were observed only in 2012; SysA cows had lower DMI and DNDFI than SysB cows during P1, perhaps because of botanical composition differences between the treatments (Table 2). In this study, cows ingested, on average, 2.6% (SysA) and 2.8% (SysB) of their BW. These values are in agreement with previous studies.

Marston et al. (1998) suggested that DMI of lactating beef cows varies from 2.3% to 2.7% of BW for cows grazing average and high-quality forages, respectively.

Hatfield et al. (1989) observed DMI from 14.8 to 16.1 kg DM/d (on average, 2.8% of BW) for lactating beef cows with different milk production levels.

Although forage quality (Table 3) generally decreased in SysA pasture sites during P2, DMI did not change between systems (during P2). We believe that despite the poorer forage quality observed in SysA, cows grazed selectively for higher-quality forage. The forage composition analysis indicated the presence of legumes during P2 in both systems. Herbage mass analysis indicated no treatment differences. In addition, in SysA cow-calf pairs were moved to a new paddock 3 times daily, and they were moved once daily in SysB. We believe that these combined factors (presence of legumes, high herbage mass, and rotational schedule) provided the opportunity for cow-calf pairs to select what to eat and match nutritional requirements.

CH4 Emissions from Beef Cows Emissions are described as daily emissions per cow (g CH4/d), daily emission mass expressed per unit of intake (GEI, NDFI, and DNDFI), or unit of metabolic

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Table 4. Body weight, DMI, GE intake, NDF intake, and digestible NDF intake of cows grazed with different grazing management strategies Period2 System1

1

Effect (P-value) 2

2012 grazing season BW, kg/cow SysA 526.2 550.7* SysB 529.5 558.6* SEM 20.3 DMI, kg/d SysA 11.3a 15.1* SysB 15.0b 14.9 SEM 0.7 GEI,3 MJ/d SysA 198.0a 268.4* SysB 264.7b 265.2 SEM 13.3 NDFI,4 g/kg SysA 6.7a 11.2a,* b SysB 8.7 8.8b SEM 0.5 DNDFI,5 g/kg SysA 4.9a 6.6* SysB 6.4b 6.1 SEM 0.3 2013 grazing season BW, kg/cow SysA 563.9 578.3* SysB 559.4 570.8 SEM 19.5 DMI, kg/d SysA 14.3 17.9* SysB 15.3 17.7* SEM 0.6 GEI, MJ/d SysA 254.6 313.1* SysB 272.6 308.8 SEM 10.1 NDFI, g/kg SysA 9.3 11.8* SysB 10.1 10.7 SEM 0.4 DNDFI, g/kg SysA 6.7a 4.3* SysB 7.2b 4.8* SEM 0.2 a,bMeans

Treatment Treatment Year Period Ă&#x2014; period 0.85

<0.01 <0.01

0.14

0.05

0.05

0.39

<0.01

0.69

0.42

0.35

<0.01

0.25

0.01

0.03

<0.01

0.03

<0.01 0.01

<0.01

0.83

<0.01 <0.01

0.66

0.70

0.05

0.01

<0.01

0.05

0.42

0.35

<0.01

0.23

0.01

0.01

<0.01

0.01

<0.01 <0.01

0.01

within a column with different superscripts differ (P < 0.05). *Means within a row with different superscripts differ (P < 0.05).

1SysA: 1 cow/ha stocking rate and 112,000 kg BW/ha stocking density; SysB: 2.5 cows/ha stocking rate and 32,000 kg BW/ha stocking density.

2June 4 to 10, 2012 (period 1), September 4 to 11, 2012 (period 2); June 5 to 10, 2013 (period 1), September 11 to 17, 2013 (period 2).

3GEI: GE intake.

4NDFI: NDF intake.

5DNDFI: digestible NDF intake.

BW (g CH4/kg BW0.75). Although year effects were not observed, data are presented by year (Table 5) because of cannulation in 2013 and for consistency with reporting of other data. A treatment by period interaction was observed for CH4/NDFI in both years and CH4/DNDFI in 2013. During P1 of 2012, enteric CH4 emission mass and per unit GEI, DMI, and NDFI were higher from cows grazed in SysA than from cows grazed in SysB during P1 (Table 5), perhaps the result of lower DMI observed for SysA cows during P1 of 2012 (Table 4).

The highest daily emissions were observed from SysA cows during P1 (249 g CH4/d), which might have led to greater loss of CH4 as a percent of GEI. In this study, cows were cannulated in 2013, and therefore, sampling was conducted on noncannulated cows in 2012. Cannulas were included in 2013 to retrieve the permeation tubes at the end of the grazing season and to confirm that the SF6 release rate was maintained throughout the grazing season. Beauchemin et al. (2012) observed different enteric CH4 emissions from cannulated vs. noncannulated cows, suggesting that values observed in this study may underestimate actual CH4 emissions. However, in previous unpublished work, the authors used a chamber technique to confirm that emission measurements using the SF6 method are comparable to chamber measurements for the same animal when they are collected simultaneously. The permeation tubes used for these beef cows were first checked for SF6 release rate using cannulated steers in chambers, approximately 1 mo before implantation in the beef cows. Data showed a less than 3% difference between methods, with that difference due in part to cannula leakage. It is appropriate to assume a similar leakage from the cannulas in the study animals.

Enteric CH4 emissions in this study are within the range reported by others using the SF6 tracer method from yearling heifers, first-calf heifers, and mature cows (120 to 255 g CH4/d; De Ramus et al., 2003) and cows and steers (150 to 240 g CH4/d; Pavao-Zuckerman et al., 1999). McCaughey et al. (1999) reported CH4 emissions varying from 267 to 294 g CH4/d from first-calf, early lactation heifers grazing grass-only and alfalfa pastures. Our results ranged from 153 to 327 g CH4/d across treatments and were lower than the values indicated by McCaughey et al. (1999).

These authors also observed higher emission rates per unit BW0.75 (2.6 g CH4 kg/BW0.75) and higher loss of CH4 as a percentage of GEI, varying from 7% to 9.5%.

Pinares-Patino et al. (2007) conducted an experiment to compare CH4 emissions from grazing heifers managed under high (2.2 livestock units, LU) and low (1.1 LU) stocking rates. They observed daily emissions (on

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Table 5. Methane emissions from cows grazed with different grazing management strategies Period2 System1

1

Effect (P-value) 2

2012 grazing season CH4, g/d

SysA 372.7a 258.6 SysB 181.5b 153.6 SEM 36.5 CH4, g/kg DMI SysA 26.2a 16.1 SysB 11.3b 10.8 SEM 3.0 CH4,3 % GEI SysA 8.4a 5.0 SysB 3.7b 3.2 SEM 0.9 CH4,4 g/kg BW0.75 SysA 3.3 2.3 SysB 1.7 1.4 SEM 0.5 CH4,5 g/kg NDFI

SysA 40.2a 21.8* SysB 17.1b 17.8 SEM 6.0 CH4,6 g/kg DNDFI SysA 60.2 38.5 SysB 25.1 24.3 SEM 9.5 2013 grazing season CH4, g/d SysA 185.7 176.1 SysB 158.7 275.1 SEM 26.3 CH4, g/kg DMI SysA 16.8 9.6 SysB 10.7 14.8 SEM 2.5 % CH4,3 GEI 5.3 SysA 3.1 SysB 3.3 4.8 SEM 0.9 CH4,4 g/kg BW0.75 SysA 1.6 1.5 SysB 1.4 2.3 SEM 0.6 CH4,5 g/kg NDFI SysA 28.2 14.4* SysB 18.4 24.4 SEM 5.4

Treatment Treatment Year Period Ă&#x2014; period 0.04

0.15

0.10

0.31

Table 5. Continued.

Period2 System1

1

Effect (P-value) 2

CH4,6 g/kg DNDFI SysA 38.8 40.1 SysB 24.3 55.1* SEM 9.4

Treatment Treatment Year Period Ă&#x2014; period 0.06

0.53

0.04

0.05

a,bMeans

0.02

0.06

0.08

0.11

0.03

0.13

0.08

0.20

0.40

0.20

0.08

0.29

0.02

0.31

0.05

0.04

0.06

0.53

0.14

0.17

0.35

0.15

0.16

0.10

0.89

0.06

0.44

0.07

0.81

0.13

0.56

0.06

0.5

0.20

0.24

0.11

0.97

0.31

0.27

0.01

Continued.

within a column with different superscripts differ (P < 0.05). *Means within a row with different superscripts differ (P < 0.05).

1SysA: 1 cow/ha stocking rate and 112,000 kg BW/ha stocking density; SysB: 2.5 cow/ha stocking rate and 32,000 kg BW/ha stocking density.

2June 4 to 10, 2012 (period 1), September 4 to 11, 2012 (period 2); June 5 to 10, 2013 (period 1), September 11 to 17, 2013 (period 2).

3GEI: GE intake.

4BW0.75: metabolic BW. 5NDFI: NDF intake.

6DNDFI: digestible NDF intake.

average, 216.6 g CH4/d) similar to but a CH4 emission rate (7% of GEI) higher than in the present study.

Johnson et al. (1994) suggested CH4 losses as a percentage of GEI varying from 2% to 12% for cattle fed diets with different compositions. Published values for grazing cattle are approximately 6% and represent studies including steers (Kennedy and Charmley, 2012) and heifers (Boadi and Wittenberg, 2002; Chaves et al., 2006). Cattle eating high-forage diets typically release a greater percentage of their dietary energy as CH4 than cattle eating grains (Freetly and Brown-Brandl, 2013). Enteric CH4 emissions range from 3% for feedlot cattle to 6% of GEI lost as CH4 for grazing cattle (Intergovernmental Panel on Climate Change, 2006). Kurihara et al. (1999) observed CH4 emission rates as high as 11% of GEI. Our values varied from 3.2% to 8.4% of GEI, which could be considered low for grazing lactating beef cows. The highest value (8.4% for SysA cows during P1 of 2012) was a result of high daily emission combined with low DMI. The emission rate is a function of DMI; higher DMI explains the lower emission rate as a percentage of GEI. Cows in the present study had comparatively higher DMI than animals from previous studies focusing on enteric CH4 emissions. McCaughey et al.

(1999) observed heifers with 10 kg/d DMI on average.

Pinares-Patino et al. (2007) reported heifers consuming, on average, 9 kg DM/d, whereas our cows consumed, on average, 15 kg DM/d. Daily CH4 production increases with DMI (Boadi et al., 2002). Ricci et al. (2013) studied the correlation between daily CH4 emissions and several variables such as BW0.75, DMI, GEI, CP, NDF, and lignin. Stronger correlations were observed for DMI and GEI (on average, r = 0.83). A weaker correlation was found for BW0.75 (r = 0.64).

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Chiavegato et al.

Crude protein and NDF were correlated but not as strongly (r = 0.04 and 0.20, respectively). These results suggest that intake-related variables individually explained a substantial proportion of the variation in observed CH4. Therefore, we expected to observe higher daily CH4 emissions because DMI was higher than reported in previous research, and we expected emissions to increase from P1 to P2 given that intake increased and overall forage quality decreased during P2.

Bannink et al. (2010) developed simulations to verify the impact of fertilization and grass stage of maturity on cattle emissions. They concluded that the quality of the forage had more effect on CH4 emission than DMI. This concept is applicable to our results.

The rotational grazing management implemented provided new paddocks every 8 to 12 h (SysA) or each day (SysB). The rotational grazing practice allowed cows to select for higher-quality forage at every feeding event. Consequently, despite the higher DMI in this study than in previous research, it is likely that cows in the present study were selecting high-quality forage and produced comparatively lower CH4 emissions.

It has been suggested that continuous set stocking allows maximum selective grazing, which results in higher response per animal than rotational grazing (Matches and Burns, 1995). De Ramus et al. (2003) have suggested that when forage quality is low, low stocking density and continuous stocking allow the animals to select “portions of the forage plant” that are higher in quality. Pur results support the suggestion

by De Ramus et al. (200), mainly because continuous grazing reduces forage diversity, which requires animals to seek portions of the plants with higher quality, this phenomenon is fleeting because of increasing plant maturity and selective grazing. Selective grazing is the cause of uneven usage of pasture (Teague et al., 2004), which leads to overgrazing and diversity reduction under continuous grazing practices (Teague et al., 2013).

In our pasture sites, forage diversity was high enough that cows were able to select higher-quality plants instead of higher-quality portions of the same plants.

Conclusion Forage composition analysis showed that both grazing systems implemented in this study allowed the development of grasses and legumes throughout the grazing season. The grazing system with shorter rest periods (SysB) had a greater proportion of legumes and greater forage quality overall at the end of the season than the grazing system with longer rest periods (SysA). However, decreased forage quality at the end of the grazing season did not decrease DMI of cows

in SysA. We believe that both grazing systems implemented in this study provided opportunities for selective grazing of different plant types, without overgrazing, even though overall forage quality decreased at the end of the grazing season in pastures grazed with a low stocking rate and high-stocking-density system.

Our results indicate that grazing management did not affect daily CH4 emissions from lactating beef cows. Therefore, the initial hypothesis that SysA would have greater CH4 emissions, mainly during P2, was not confirmed. Additionally, CH4 emissions tended to be lower than reported values for lactating beef cows. The selective grazing resulted from the management systems implemented in this study, which allowed cows managed with different grazing strategies to eat forage with similar qualities that met nutritional requirements with no difference in CH4 emissions. The results in this study suggest that forage quality might be a better predictor of daily CH4 emissions than DMI. Further research is needed to confirm that hypothesis.

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