UC Davis

Agricultural production, especially in the case of livestock like dairy, is a significant contributor to climate change through the production of greenhouse gases (GHG) like carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), as well as other air pollutants like ammonia (NH3). However, increasing productivity led the U.S. dairy industry to reduce its negative environmental impacts through intensification or dilution of maintenance. In chapter 2, the aim of the study was to update a previous environmental impact assessment comparing the carbon footprint of Cheddar cheese production by Holsteins versus Jerseys in 2009. The functional unit of this analysis was 1 million metric tons of energy-corrected milk. A deterministic model simulated the two populations, establishing 16 life groups per breed needed to meet the functional unit. Production metrics like milk yield and milk components were sourced from the Council of Dairy Cattle Breeding National Metrics database for the year 2020. The software AMTS.Cattle.Pro was used to design total mixed rations for each individual life stage and breed to meeting the energy and nutritional needs of each animal. Feeds informed the background systems used to calculate land and water use, and associated feed emissions were calculated via economic allocation with emission factors sourced from ecoinvent 3.10. AMTS.Cattle.Pro also estimated daily nutrient and manure excretion, daily enteric CH4, and voluntary water intake. Total GHG emissions were converted to carbon dioxide equivalents (CO2e) using AR6 global warming potential (GWP) values. The results found milk yield increased for Holsteins and Jerseys by 29% and 26%, respectively, from 2009 versus 2020. Certain key performance indicators like land use decreased for Jerseys and Holsteins due to less feed intake associated with smaller populations. However, water use increased because irrigation was included in this assessment, but not in the original 2009 study. Overall, the carbon footprint for Jersey milk production was 1.6 kg CO2e/kg ECM and the carbon footprint for Holstein milk production was 1.8 kg CO2e/kg ECM, which was higher than the carbon footprints of the original assessment, likely due to the different GWP values used. Overall, both breeds made advancements in productivity, helping to offset increases in resource consumption. And the carbon footprint for Jersey production was 89% of the Holstein carbon footprint, meaning the Jersey population had an environmental impact more similar to Holsteins in 2020 compared to their environmental impacts in 2009. In chapter 3, the aim of study 2 was to investigate the effects of Eminex® on GHG and NH3 emissions from fresh dairy slurry and dairy lagoon water. Eminex® had previously reduced total GHG emissions by 99% under anaerobic and temperature-controlled conditions, but had not tested in liquid-based systems. For experiment 1, feces and urine were collected from lactating dairy cows and mixed into a homogenous slurry, prior to being allocated into twelve individual bowls with 2.2 kg/bowl. Each bowl was randomly assigned a treatment: high, low, and a control with an n = 4/group. Upon receiving treatment, bowls were sampled beneath individual OdoFlux chambers for 7 days to measure for CO2, CH4, N2O, NH3, and ethanol (EtOH) emissions. Samples were collected to determine changes to manure quality. For experiment 2, lagoon water was collected from a commercial dairy, and distributed to 12 stainless 208-L stainless steel barrels. Two treatments (n = 4/treatment) were administered: high (1 kg/m3 lagoon water), and low (0.5 kg/m3 lagoon water); and control (n = 4). Four barrels at a time were sampled over two, nonconsecutive 14-day periods, using OdoFlux chambers, monitoring CO2, CH4, N2O, and NH3. Slurry total solids, total nitrogen, and total carbon was similar across all treatment groups (P > 0.05). Acetic acid concentration in slurry increased in Eminex® treated groups compared to control (P < 0.05). All slurry GHG emissions, except for N2O, declined (P < 0.05). Results showed that the high Eminex® treatment compared to control reduced CO2, CH4, and NH3 emissions by 49.3%, 30.4%, and 34.9%, respectively (P < 0.05). In lagoon water, total nitrogen increased with treatment (P < 0.05), while total solids and total carbon remained similar between all three treatments (P > 0.05). Volatile fatty acid concentration in lagoon water also saw a trend for increasing acetic acid concentration in Eminex® treated groups compared to control (P < 0.1). GHG emissions from lagoon water also decreased over time (P < 0.05). The high Eminex® treatment emitted 12.0% less CO2 (P < 0.1), 85.1% less CH4 (P < 0.05), and 82.7% less N2O (P < 0.05). However, both Eminex® treatments, compared to control, increased NH3 volatilization over time (P < 0.05). With improvements to manure composition with increasing nitrogen content, as well as significant reductions in GHG emissions, Eminex® is a promising manure additive that could mitigate the negative environmental impacts of manure management systems. Further research is needed to continue verifying its potential in different settings and at the commercial level. The final study in Chapter 4 investigated the effects of Eminex® on the microbiome of slurry and lagoon water. Samples were collected from fresh slurry and dairy lagoon water during study 2. Samples were DNA extracted prior to being sent out to an independent laboratory for shallow shotgun metagenomic sequencing (SSMS). Results of the SSMS showed that the relative abundance of the phylum Proteobacteria decreased with Eminex® treatment in lagoon water, but increased in relative abundance with Eminex® treatment in slurry. Other phyla, like Firmicutes and Actinobacteria increased in relative abundance with Eminex® in lagoon water, but not in slurry. Pathogenic phyla, like Fusobacteria, did not increase in relative abundance with Eminex® treatment in slurry, but increased substantially in untreated slurry. A principal component analysis (PCA) was also performed and confirmed distinct microbiomes between slurry and lagoon water. The PCA also noted that the high Eminex® treatment, compared to the low Eminex® treatment, elicited a faster microbiome change. This suggested that Eminex® could be applied more effectively earlier in the manure management chain. Lastly, a linear discriminatory analysis showed that bacterial populations were at their highest in the two Eminex® treatments at day 28, and highest in the control by day 56. Ultimately, the Eminex® treatment resulted in significant changes to the manure microbiome, helping to explain how this additive reduces GHG and NH3 emissions. Looking at the microbiome demonstrated that the Eminex® doses can be decreased and still be effective. Future research would benefit from exploring the metabolomics associated with microbes exposed to Eminex® and exploring the effects of different treatment protocols of emissions and the manure microbiome.