Final Report for GNE15-097
In the Northeast, early spring, mid-late summer, and late-fall are periods when pasture mass is decreased. The purpose of this study was to determine if the addition of alternative forage crops (AFC) alter rumen microbes, feed efficiency (FE), and animal performance of 16 lactating organic Jersey dairy cows during spring and summer periods of low pasture mass. Two independent experiments were performed in May (spring) and July (summer) 2015 at the University of New Hampshire Organic Dairy Research Farm. Cows consumed either a control (CON; legume/grass mixture), or a treatment (TRT) cool-season grass pasture, 30% strip-tilled with AFC, for a 21d period. The estimated dry matter intake (DMI) of spring AFC (barely, wheat, rye, triticale, hairy vetch) was 1.7 kg/d (7.3% of total DMI). Spring pasture and total mixed ration (TMR) DMI and FE did not differ between CON and TRT groups. The estimated intakes of crude protein (CP) and neutral detergent fiber (NDF) did not differ between groups. In the spring, no differences between groups were observed in animal performance, rumen microbial numbers, or rumen volatile fatty acids (VFA). Few bacteria but no protozoa genera differed between groups. Relative abundances of the methanogen species, Methanobrevibacter ruminantium were lower in TRT (9.3% of total methanogen 16S sequences) cows than in CON (13.9%) cows, while abundance of Methanobrevibacter millerae were greater in TRT cows (11.2%) than in CON cows (8.5%).
Total DMI of summer AFC (buckwheat, chickling vetch, oat) was 1.2 kg/d (5.7% of total DMI). No differences were observed in milk yield, rumen bacteria and protozoa numbers, or most abundant VFA. Rumen methanogen numbers were greater in CON cows (6.85 log10 16S copies/mL rumen digesta) than in TRT (6.41) cows (P = 0.045). Milk fat % and energy-corrected milk (ECM) were greater in TRT (4.8%, 22.7 kg/d) cows than CON (4.4%, 19.4 kg/d) cows (P < 0.05). Abundances of the methanogen species, Methanosphaera (Msp.) stadtmanae and the bacterial genus Coprococcus were lower in TRT cows (1.1%, 1.5%, respectively) than in CON (1.5%, 1.9%, respectively, P < 0.05) cows. Abundances of protozoa taxa did not differ between groups.
In conclusion, before supplementation of cool-season grasses with AFC can be recommended to farmers in the Northeast for improved animal performance and maintenance of rumen function, we suggest a greater inclusion of AFC on pasture for future studies.
The purpose of this project was to provide scientific knowledge to Northeast organic dairy farmers about the potential benefits of feeding diverse AFC during periods of low pasture mass. Our project addressed two key areas in sustainable agriculture: 1) improved productivity and the reduction of on-farm costs and 2) the reduction of environmental impacts in agriculture. We also addressed the first areas by providing new information in regards to the relationship between the rumen microbiota and FE of lactating dairy cattle consuming a legume/mixed grass pasture versus a pasture 30% strip-tilled with a mixture of AFC. To address the second areas, we identified and quantified the rumen methanogens that contribute to methane emissions.
Although 80% of all U.S. certified organic dairy farms are located in the Midwest and Northeast, the Northeast’s herd sizes are smaller and their milk production is lower than those in the West . Therefore, it is necessary to perform research that enhances aspects of production in the Northeast region and providing farmers with new nutritional and grazing strategies. During the Northeast grazing season, there are three periods of decreased forage biomass: early spring, mid-summer, and late fall. Organic farmers, requiring 120d of pasture , are interested in using diverse pastures of AFC to overcome periods of low herbage biomass (e.g., brassicas, millet, and small grains). Thus, we aimed to determine if AFC offered in addition to legume-based pastures during typical “slump” periods would alter the rumen microbiota, maintain FE (i.e., the ability to convert feed nutrients into milk), and milk production and solids (protein and butterfat) upon which farmers receive premiums.
Few studies have explored the rumen microbes in relation to FE [3, 4], while no studies have investigated this relationship in lactating dairy cattle consuming different AFC. Our previous research identified and quantified the rumen microbes found in three typical dairy breeds of primiparous cattle over a lactation period[5–7]. The animals were fed a 70:30 forage to concentrate total mixed ration (TMR) diet, but we did not explore the role of a pasture diet on the rumen microbiota. Our results showed that the rumen microbiota at 3 days in milk (DIM) differed from that at 273 DIM [7, 6]. The cows at 3 DIM were on a high-fiber pre-partum diet before changing to a high-starch post-partum diet. Because organic dairy farmers cannot rely on a 100% TMR diet and need to implement pasture, it is pivotal to investigate a dietary strategy that is more suitable for an organic farm.
Rumen methanogens produce methane gas that is expelled from the mouth of the ruminant. In the U.S., methane emissions from ruminants are the second largest anthropogenic source of methane, accounting for 25% of all methane emissions that contribute to climate change . Furthermore, the release of methane causes the lactating dairy cow to lose 2-12% of her energy for production , thus, making her significantly less feed efficient. It is therefore important to identify the rumen methanogens so that we can link the methanogen taxa identified with high or low feed efficiencies. The advantages of a more feed efficient animal are: 1) decreased feed consumption and 2) less energy loss as a result of methane production. Therefore, a more feed efficient animal would result in a reduction in feed costs, thus increasing farmers’ profitability and reducing the animal’s contribution to climate change.
Rumen bacteria and protozoa breakdown feedstuff (e.g., cellulose and starches) into volatile fatty acids (VFA), the main energy source for cows. Moreover, rumen methanogens are attached to the protozoa and exhibit a symbiotic relationship with one another. High feed efficient mid-to-late lactation dairy cows had lower abundances of specific rumen methanogen species . Therefore, these two studies indicate the importance of both the rumen environment and the types of microbes that maybe influencing methane emissions and FE. Of the three microbe types, rumen protozoa are studied the least. No studies have examined as to whether rumen protozoa contribute to FE or milk production. To fill this gap in protozoal knowledge; we will identify and quantify the rumen protozoa and determine if they are associated with FE and milk production. This will enable us to better understand the unique symbiotic relationship between the different types of rumen microbes.
If the addition of AFC in dairy cows’ diet demonstrates an increase in FE and farmer profitability, they can be used instead of 100% legume-based pastures to overcome periods of low herbage.
Although the first two experiments went according to plan, the third experiment (fall) did not. We planned to start the third experiment in late September/early October, however, the pasture growth was very poor for both the TRT and CON groups as a result of drought and cooler nights than normal. Since the pasture biomass was not enough, the animals needed to be taken off the pasture, thus ending the project.
The information provided below will discuss the fulfillment of objectives from the spring and summer experiments.
Objective 1. Quantify feed efficiencies of each animal. The specific aim was to compare the feed efficiencies of the experimental group (25% AFC, 25% legume-pasture, and 50% TMR) to a control group (50% legume-pasture and 50% TMR) during early spring, mid-summer, and late fall.
Objective 1 was completed in February 2016 for both the spring and summer experiments. The analyses of dry matter intakes using chromium oxide were completed at the University of New Hampshire. FE was calculated as energy corrected milk (ECM)/DMI.
Objective 2. Identify and quantify the rumen bacteria, methanogens, and protozoa. We collected whole rumen digesta samples to delineate what microbes (diversity) and how many are present (density).
The specific aims of this objective are to a) determine the species of each type of rumen microbes with DNA sequencing, b) estimate the rumen microbial densities via real-time polymerase chain reaction (PCR), and c) determine if certain microbial taxa relate to FE.
Objective 2 was completed in June 2016.
Objective 3. Measure the rumen fermentation products. We are interested in the rumen fermentation products because they contribute to the environment of the microbes and are key to their growth, maintenance, and survival. For example, some microbes prefer a higher pH optimum than other microbes or produce a different volatile fatty acid profile. Therefore, if we know what environment the rumen microbes are living in, we can better understand why they thrive over other microbes.
The specific aims of this objective are to a) measure the chemical (nutrient) composition of the experimental diets, b) quantify the VFA profile (rumen microbial fermentation by-products) via gas-liquid chromatography (GLC), and c) measure rumen pH.
Objective 3 was completed in March 2016. Rumen fluid samples were sent to the West Virginia University Rumen Fermentation Profiling Laboratory for VFA analyses.
Objective 4. Determine if the incorporation of AFC will benefit dairy farmers. The specific aims of this objective were to a) compare feed costs, DMI, and cow performance (milk yield, butterfat, and protein) between the legume-based pasture (CON) and AFC-based feeding groups, and b) determine if the milk check is affected by the different diets and time periods.
Objective 4 was partially completed. DMI estimates of TMR and pasture were determined from the chromium oxide measurements. Milk, protein, and fat percentages were analyzed by Lancaster Dairy Herd Improvement Association (Manheim, PA) and yields were calculated. As no changes were observed in animal performance from the spring experiment, we know that in the milk check was unaffected. Though milk fat % was increased at the end of the summer experiment, we cannot say that this affected the milk check. Future work, needs to measure the milk solids on a weekly basis instead at the end of the 21d experiment.
In two separate experiments (Spring, May 2015) and (Summer, July 2015) 16 lactating Jersey cows on the Organic Research Farm in Lee, NH (n=58) were assigned to one of two treatments in a randomized complete block design. Cows were matched by parity, stage of lactation, and milk production. Each experiment included a covariate period prior to the 21d experimental period, with 8 cows in the CON group and 8 cows in the TRT group. Covariate period samples were collected 3-4d before the experimental periods began. Experimental period samples were collected during the last 7d of each 21d period.
1. Quantify feed efficiencies of each animal
The FE of each cow was calculated as kg of milk produced per kg dry matter consumed., We used the ECM yield, which includes milk production and corrects for protein and fat components to calculate the FE of each cow. During each period, milk and milk weights (kg) were collected for four consecutive milkings (d19-d21). For 10 days, each cow consumed 1 kg/d of pelleted chromium oxide, an external marker to estimate dry matter intake (DMI). Fecal samples were collected during the last 5d of each experimental period and the percent of chromium oxide in the feces was used to calculate the DMI.
Four handfuls of TMR were collected before fed to the each cow in the Calan door system (America Calan Inc., Northwood, NH) to determine the DM% of TMR. The TMR samples for each period were mixed in a bin and quartered. For each period, the same quarter (e.g., quadrant 1) was collected. The DM% of the feed was calculated (100-% moisture). The as-fed intakes (kg) of each animal were multiplied by the DM% to determine DMI. Prior to the covariate period, the cows were trained to their own feed bin and had collars around their necks with transponders enabling them to open their own bin.
- Identify and quantify rumen bacteria, methanogens, and protozoa
On d20 and d21 of each 21d experiment, whole rumen digesta samples were collected (500 mL) after AM milking at 0600h. One rumen sample was collected from each cow. Samples were collected via an Institutional Animal Care and Use Committee-approved stomach intubation method.
A 15mL aliquot of whole rumen digesta was immediately frozen at -20°C to lyse microbial cell membranes and release DNA. The microbial DNA was extracted from 250μL of whole rumen digesta and cleaned (i.e., removing PCR inhibitors) with the QIAamp Mini Stool Extraction kit and repeated-bead plus column method. The PCR products were purified with the QIAquick Gel Extraction kit. The purified DNA was quality checked before it was sent to the sequencing facility. The concentration of the purified DNA (ng/μL) was 20 or greater. All purified DNA PCR products were submitted for next-generation sequencing (i.e., DNA sequencing) with Illumina MiSeq version 3 at Molecular Research Laboratories (Shallowater, TX). Once sequences were received, all bioinformatics analyses were performed in-house with the program MOTHUR. These analyses were used to determine which microbes were present and to define the diversity of each data set.
- ii) The 16S rRNA gene was targeted to identify the rumen bacteria and methanogens because it is highly conserved and less likely to mutate. The 16S rRNA gene has nine variable regions that contain sequences specific to certain microbial species. For example, the bacterium coli would have a different variable region than the bacterium S. aureus. Therefore, we used bacterial-specific forward and reverse primers (i.e., short nucleic acid sequences) 27F-519R and methanogen-specific primers 86F-474R to target the V1-V3 variable regions of the 16S rRNA gene. Known bacterial and methanogen DNA sequences were compared to the unknown sample sequences to identify the methanogen species. A previously developed bioinformatics protocol and the general MOTHUR MiSeq standard operating procedure were used to perform sequence analyses.
iii) The 16S rRNA gene was targeted to quantify the bacterial density, however, the mcrA gene was targeted to quantify the methanogen density. The mcrA gene is involved in the production of methane and is specific to methanogens. The log10 of the PCR copy number of the mcrA gene/mL rumen digesta was calculated by real-time PCR. Therefore, it is a good marker to estimate how many methanogens are in each sample.
- iv) The V3-V4 variable regions of the 18S rRNA gene were PCR amplified with the primer pair P-SSU316F-GIC758R to identify the rumen protozoa and a similar bioinformatics workflow mentioned above was followed. The 18S rRNA gene is targeted in protozoa because they do not contain a 16S rRNA gene.
- v) The 18S rRNA gene was amplified by real-time PCR to determine the protozoal density (protozoal cells/mL rumen digesta).
- Measure factors that influence the rumen environment
Because we identified and quantified the rumen microorganisms, it was important to measure the factors that influence their environment, such as rumen VFA and pH.
- i) Fifty mL of rumen digesta was used to determine the VFA concentrations and proportions of each cow during each time period. Whole rumen digesta was filtered through a layer of cheesecloth immediately after sample collection, 50% (vol/vol) H2SO4 was added, and stored at -20 in the laboratory until further analysis by West Virginia University Rumen Fermentation Profiling Laboratory (Morgantown, WV). GLC was used to measure VFA concentrations. The concentration of total VFA, acetate (A), propionate (P), butyrate, isobutyrate, and isovalerate were measured and the A:P ratio was calculated. The A:P ratio is of interest because lower ratios indicate greater propionate concentrations that have shown lowered methane emissions and greater FE in ruminants.
- ii) Immediately after rumen digesta was collected, all saliva contamination was poured off. A portable pH meter was used to measure rumen pH on-farm.
Although we measured the pH immediately after sample collection, the whole rumen digesta collected on pasture was frothy and we believe that saliva contamination was still an issue when reporting rumen pH. pH values between 6.8-7.4, may have been overestimated as saliva contains sodium bicarbonate, a weak base that increases pH.
iii.) Feed samples were collected and composited for each time period. For the pasture nutrient composition, we sent the samples to Cumberland Valley Analytical Services (Hagerstown,MD) for analyses. The specific nutrients quantified were: crude protein, ash corrected neutral detergent fiber (NDF), acid detergent fiber, sugars, lignin and minerals. A total of 60 samples was collected from each pasture for botanical composition estimates on d18-d21.
- Determine if the incorporation of an AFC will benefit dairy farmers
- i) For each period, we planned to calculate the cost to grow a legume-based pasture in comparison to the AFC, however, this goal was not achieved. We determined the DMI of the CON and TRT cows.
- ii) To estimate the milk check premiums that the farmer would receive, we planned to measure milk yield and components (i.e., pounds of butterfat and protein).
On d19-d21, we measured individual milk production and milk solids for four consecutive milkings but were not able to determine if the milk check was influenced by AFC consumption. In hindsight, measuring milk production and solids over the 21d experiments would have better enabled us to determine in AFC influenced the milk check.
Experiment 1 (Spring)
FE– Individual cow FE were calculated as the ECM/DMI and did not differ between CON (1.4) and TRT (1.5) groups. ECM and DMI of the CON (26.8 kg/d, 19.3 kg/d, respectively) and TRT groups (28.3 kg/d, 19.35kg/d, respectively) did not differ.
Botanical and Nutrient Compositions- TMR consisted of 60:40 forage:concentrate and was 57% of the total DMI. The estimated DMI of CON pasture components were: grasses (30% of total DMI), legumes (7%), forbs (6%) and the TRT pasture components were: grasses (26%), legumes (6%), forbs (4%), AFC (7%). On a DM-basis, the CON pasture was 16.0% CP, 53.3% NDF, 0.8% starch, and the TRT pasture was 15.1% CP, 56.0% NDF, 0.3% starch.
Animal Performance- Milk production and solids did not differ between CON and TRT groups.
Rumen Environment and Microbiota
The most abundant bacterial phyla, Firmicutes and Bacteroidetes, did not differ between the CON (53.8%, 43.1%) and TRT (54.1%, 43.4%) groups. Relative abundance of the bacterial genus, Oscillibacter was lower in CON (1.1%) cows than in TRT (1.4%) cows (P = 0.02), while the genus Lachnospira tended to be greater in CON (2.7%) cows than in TRT (2.2%) cows (P = 0.07). Prevotella was the most abundant bacterial genus (CON: 19.6%, TRT: 18.7%). Protozoal genera did not differ between groups. The protozoal genus Epidinium tended to be greater in CON (1.6%) cows than in TRT (1.0%) cows (P = 0.09). Abundances of the methanogen species, Methanobrevibacter ruminantium were lower in TRT (9.29%) cows than in CON (13.85%) cows, while abundances of Methanobrevibacter millerae were greater in TRT (11.17%) cows than in CON (8.50%) cows. Methanogen species can belong to two different ancestral taxonomic clades, Methanobrevibacter smithii-gottschalkii-millerae-thaueri (SGMT) and ruminantium-olleyae (RO). The CON cows had a lower SGMT:RO ratio than the TRT cows (6.1 versus 10.2; P = 0.02). Shannon and Inverse Simpson Diversity indices from bacteria, protozoa, and methanogens, as well as rumen VFA proportions did not differ between CON and TRT groups.
Experiment 2 (Summer)
FE- FE did not differ between CON (1.0) and TRT (1.1) groups. Total DMI of the CON (19.6 kg/d) and TRT groups (20.4 kg/d) did not differ. ECM was lower in the CON cows (19.4 kg/d) than in the TRT cows (22.7 kg/d).
Botanical and Nutrient Compositions- DMI of TMR for the CON (56% of total DMI) and TRT (57%) groups did not differ. The estimated DMI of CON pasture components were: grasses (30% of total DMI), legumes (5%), forbs/dead material (9%) and of the TRT pasture components: grasses (27%), legumes (5%), forbs/dead material (5%), AFC (6%). On a DM-basis, the CON pasture contained 12.9% CP, 53.1% NDF, 1.0% starch and the TRT pasture contained 13.8% CP, 50.1% NDF, 2.2% starch.
Animal Performance- Milk production, protein % and yield, and fat yield did not differ between groups. Milk fat% was greater in the TRT cows (4.8%) than in CON cows (4.4%) (P = 0.04).
Rumen Environment and Microbiota
The most abundant bacterial phyla, Firmicutes and Bacteroidetes, did not differ between CON (50.3%, 46.4%, respectively) and TRT (52.1%, 44.1%, respectively) groups. Abundance of the bacterial genus Coprococcus was greater in the CON cows (1.9%) than in TRT cows (1.5%; P < 0.05), while the unclassified members of the Ruminococcaceae family tended to be lower in the CON cows (3.6%) than in the TRT cows (4.4%; P = 0.07). Protozoal genera did not differ between groups. The protozoal genus Metadinium tended to be lower in the CON cows (1.3%) than in the TRT cows (4.1%; P = 0.09). Abundances of the methanogen species Msp. stadtmanae were lower in the CON cows (1.5%) than in the TRT cows (1.1%), whereas Shannon and Inverse Simpson indices were greater in CON cows (0.21, 1.05, respectively) than in the TRT cows (0.17, 1.04, respectively; P < 0.05). Rumen proportions of the most abundant VFA acetate, propionate, and butyrate did not differ between the CON and TRT groups. Proportions of isobutyrate were greater in the CON cows (0.98%) than in the TRT cows (0.80%) cows (P < 0.05).
Discussion of Results
The aims of both experiments were to 1) compare the FE between the TRT and CON groups, 2) identify and quantify the rumen microbes (i.e., bacteria, methanogens, and protozoa), 3) measure the rumen fermentation products (i.e., VFA), and 4) determine if the incorporation of AFC into the cows’ diet will benefit dairy farmers. We hypothesized that AFC would have better nutritional quality over cool-season grasses and would replace forbs or dead material on pasture. In both periods, however, AFC had low CP and high NDF contents. During each 21d, AFC matured and were at their reproductive stages (seed head emergence and flowering). Further, the cool-season grass pasture was 30% strip-tilled with AFC, but AFC were 6-7% of the total estimated DMI. It was unexpected that warm-season annuals millet and sorghum did not grow during the summer period. Monocultures of millet and sorghum reached maturity at the horticulture farm, demonstrating the challenge of establishing annuals amongst already established cool-season grasses.
Because spring AFC were similar in NDF and CP contents as the cool-season grasses, it was not unexpected to observe no differences in DMI, ECM, and therefore FE. In the summer experiment, buckwheat had a lower content of NDF (36%) than the cool-season grasses (56%), however, FE and estimated DMI did not differ between groups. However, Future work should use the Calan door system to determine how much of each pasture component each cow consumes. Previously, diets with different forage:concentrate ratios fed to Holstein cows on a conventional farm did not yield different ECM, DMI, or FE values  and individual cows over a lactation had variable feed efficiencies , demonstrating how difficult it is to alter FE.
Inclusion of 6-7% AFC in both experiments changed proportions of less abundant bacterial genera but did not influence rumen VFA. Typically, cereal grains, such as vegetative stage wheat, may contain 21% CP, which disrupt the rumen environment and cause frothy bloat . Consumption of 100% wheat pasture by steers lead to a greater Bacteroidetes:Firmicutes ratio and lower acetate:propionate ratio . Cereal grains from both the spring (wheat, rye, triticale, barley) and summer experiment (oat) had lower CP contents (spring: 8.5% CP; summer: 11.1% CP) along with the cool-season grass pastures, which is one potential reason why few changes were observed in the rumen bacterial and protozoal genera in the CON and TRT cows.
In the spring experiment, the Methanobrevibacter SGMT:RO ratio was greater in the TRT cows than in the CON cows, which may have been influenced by the AFC pasture maturity (i.e., cell wall carbohydrates) and substrate availability for growth. Previously, dairy cows with greater Methanobrevibacter SGMT:RO ratios produced more methane , yet lower ruminal acetate:propionate ratios and digestible NDF contents , and greater abundances of amylolytic (starch-degrading) bacteria were associated with lower methane emissions .
In the summer experiment, a greater abundance of the methanogen Msp. stadtmanae in the CON cows may have been influenced by the amount of pectin the diet. Unlike the most abundant genus Methanobrevibacter, Msp. reduce methanol from bacterial and protozoal pectin degradation with hydrogen to produce methane . Furthermore, the species Msp. stadtmaneae has been associated with the protozoal genus Polyplastron in the ovine rumen . The abundance of this protozoal genus, however, remained unchanged between the CON and TRT cows, nor did we identify a correlation between Msp. stadtmaneae and Polyplastron. Differences in the relative abundance of this species were potentially due to differences in substrate availability or other unidentified symbioses with bacterial and protozoa genera.
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Several farmers at the Vermont Organic Dairy Producers Conference told me about their experiences with growing monocultures of AFC. One farmer mentioned that he started to grow buckwheat in the summer, but it matured too quickly and he was not able to graze the cows on it. We discussed how future studies (e.g., extension research with farmers) could increase the amount of AFC inclusion (e.g., 25% or 50% of diet) in the diet or use one type of AFC to establish an effect on animal performance or rumen microbiota and that we should determine if a 100% inclusion of vegetative cereal grains would result frothy bloat.
Education & Outreach Activities and Participation Summary
In March and April 2016, I presented a poster entitled “Rumen protozoal community structures of Jersey cows are not altered by 20 days of alternative forage consumption” at the 6th Annual Vermont Organic Dairy Producers Conference in Randolph, VT and at the University of Vermont’s Student Research Conference. At the Organic Dairy Producers Conference, I spoke to farmers and researchers about the challenges we had growing AFC during the spring and summer periods and the influence of their consumption on animal performance and rumen microbiota and VFA.
In July 2016, I presented three abstracts at the Joint Annual Meeting of the American Society of Animal Science and American Dairy Science Association in Salt Lake City, UT. I presented two interactive eposters, “Rumen protozoal community structures are not altered in lactating dairy cows offered alternative forage crops during short-term grazing experiments” and “Characterization of rumen bacterial and protozoal fatty acid compositions from lactating Jersey cows offered alternative forage crops,” and one oral presentation, “Alternative forage crops modify the composition and content of bovine milk fatty acids,” which included animal performance and nutrient content results.
Three manuscripts describing results from the present study are currently prepared for submission to scientific peer-reviewed journals
- Pasture inclusion of spring annual forages changes abundances of prevalent rumen methanogen species in lactating dairy cows. submitting to Archaea
- Summer annual forage crops offered to mid-lactation Jersey cows alter milk fatty acid contents without changing key rumen microbial fatty acids and taxa. submitting to Brit J Nutr.
- Spring annual forage crops offered to mid-lactation Jersey cows alter milk fatty acid contents without changing key rumen microbial fatty acids and taxa. submitting to Brit J Nutr.
As mentioned previously, cows consuming the summer AFC had greater milk fat % than those fed the CON. In future experiments, milk production and solids should be measured each week of the study, instead of at the end, to better determine if AFC influenced the milk check. Further, measures of DMI and FE were the same across the experiments. In both experiments, the rumen environment was maintained, as VFA and major rumen microbiota that ferment feedstuff, did not change between CON and TRT cows.
Please refer to the Impact of Results/Outcomes section.
Areas needing additional study
After completion of the two experiments, I am interested in strip-tilling cool-season grasses with 50% or full-tilling pasture with 100% AFC. Another approach would be to grow one particular AFC instead of a mixture of five AFC or to strip-till with one type of AFC. Because we had several different types of AFC, it was a challenge to determine which AFC or if the combination of AFC influenced rumen microbiota or animal performance. Rumen bacteria and protozoa are dynamic and have a large amount of inter-animal variation, suggesting greater animal numbers or time points for future studies. Because we observed changes in rumen methanogen species in both experiments, future research should measure methane emissions from individual cows and should use shot-gun sequencing techniques to better understand their functionality.