Methane emissions in dairy systems: animal category, production traits and relationship with microbial community

• Ano de defesa: 2016
• Autor(a) principal: Cunha, Camila Soares
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Viçosa
• Palavras-chave em Português: Bovino - Alimentação e rações; Rúmen - Microbiologia; Bovino - Digestiblidade; Metano; Ciências Agrárias; Zootecnia; Produção Animal
• Link de acesso: http://www.locus.ufv.br/handle/123456789/8788

Rumen bacterial, archaeal and anaerobic fungal communities of Holstein dairy heifers and cows, in a tropical system of production, were characterized through sequencing the 16s rRNA and the ITS genes. In addition, we investigated the relationship between these communities and enteric methane (CH4) emissions and productive traits, such as digestible dry matter intake (dDMI), digestible organic matter intake (dOMI), average body weight (BW), rumen pH, volatile fatty acids (VFA) and its main components, acetate, propionate and butyrate. Prepubertal heifers (PP), pubertal heifers (PB), and pregnant heifers (PG) were used in Chapter 1. Pregnant heifers emitted more CH4 than others, followed by PB and PP. Regarding CH4 emissions, the animals were split in high and low CH4 emitters. Heifers were fed a diet composed by corn silage and concentrate (corn, soybean meal and minerals). Prevotella, Ruminococcus, Coprococcus, Butyrivibrio, Clostridium, Shuttleworthia, SHD- 231, CF231, p-75-a5, Methanobrevibacter, Methanosphaera and Caecomyces communis were detected to be the core microbiome of the evaluated heifers. Families Bifidobacteriaceae and RF16 and genera Acetobacter and Coprococcus were strongly correlated with CH4 emissions. Genera Eubacterium, p-75-a5 and SHD-231 showed inverse correlations with CH4 emissions, dDMI, dOMI, BW and rumen pH. Methanobrevibacter, in archaeal community, and Orpinomyces, in anaerobic fungal, showed positive and weak correlations with CH4 emissions. On the other hand, strong and negative correlations were observed among Methanosphaera and this variable. Prepubertal and PG heifers were the most divergent groups in relation to CH4 emissions. Surprisingly, they did not differ in relative abundances of Firmicutes and Bacteroidetes, but PG had greater abundance of Methanobrevibacter and Vadin CA11 and lower abundance of Methanosphaera. None of the bacterial, archaea and anaerobic fungi which correlate with CH4 emissions showed significant correlations (P>0.10) with VFA and the individual concentrations of acetate, propionate and butyrate. Lastly, this work showed that bacterial, archaeal and anaerobic fungal communities did not covaried and the microbial communities did not covaried with volatile fatty acids concentration either. In Chapter 2, high-producing (HP), medium- producing (MP), low-producing (LP) and dry (DC) were evaluated. The forage:concentrate ratios they were fed were 50:50 for HP, 70:30 for MP, 80:20 for LP, and 90:10 for DC. Considering the intake of digestible fraction of feed, DC emitted more CH4, followed by MP, HP and LP, but the HP and LP emissions were similar. The core microbiome of the evaluated Holstein cows in tropical environment was composed by Prevotella, Ruminococcus, Butyrivibrio, Clostridium, Coprococcus, Shuttleworthia, CF231, SHD-231, Methanobrevibacter, and Methanosphaera. None of the anaerobic fungal operational taxonomic units (OTU) were found in all samples. Firmicutes and Bacteroidetes were the most abundant phyla found in the rumen of Holstein cows. For the archaeal community, Methanobrevibacter genera was the most abundant and in anaerobic fungi, most of the sequences were unclassified. The strongest negative correlations with CH4 emissions detected were with the relative abundance of family Coriobacteriaceae and S24-7 and of genera Butyrivibrio, Clostridium and Schwartzia. Positive correlations were found between CH4 emissions and families RF16 and Succinivibrionaceae. In the archaeal community, genera Methanosphaera relative abundance showed a strong negative correlation with CH4. Surprisingly, no significant correlation between CH4 emissions and Methanobrevibacter relative abundance was found. Relative abundance of genera Vadin CA11 (in archaea) and Caecomyces (in anaerobic fungi) were detected to be positively correlated with CH4 in g/day. Many families and genera from Firmicutes phylum showed positive correlations with dDMI and dOMI. None of the bacterial, archaea and anaerobic fungi which correlate with CH4 emissions showed significant correlations (P>0.1) with VFA and the individual concentrations of acetate, propionate and butyrate. The most opposite results observed in the present study were among DC and HP. Dry cows showed greater CH4 emissions in g/kg dDMI and g/kg of dOMI and, besides no differences were observed in relative abundances of Firmicutes, Bacteroidetes and Firmicutes:Bacteroidetes ratio, DC had lower relative abundance of Coriobacteriaceae, which was negatively correlated with CH4, and greater relative abundance of Succinivibrionaceae, that was positively correlated with CH4. In addition, DC had greater relative abundance of Methanobrevibacter and lower of Methanosphaera. Lastly, bacterial, archaeal and anaerobic fungal communities did no covary and VFA and microbial communities did not vary in a similar way either. Chapter 3 was composed by two trials. In trial 1, CH4 emissions were estimated from the seven previously described Holstein dairy cattle categories based on the SF6 tracer gas technique and on IPCC (2006) equations. Enteric CH4 emission was higher for the PP heifers when estimated by the equations proposed by the IPCC Tier 2. However, higher CH4 emissions were estimated by the SF6 technique for MP, HP and DC. Pubertal heifers, PG, and LP had equal CH4 emissions as estimated by both methods. In trial 2, two dairy farms were monitored for one year to identify all activities that contributed in any way to GHG emissions. The total emission from Farm 1 was 3.21 t CO2e/animal/yr, of which 1.63 t corresponded to enteric CH4. Farm 2 emitted 3.18 t CO2e/animal/yr, with 1.70 t of enteric CH4. For the carbon balance calculations, when the carbon stock in pasture and other crops was considered, the carbon balance suggested that both farms are sustainable for GHG, by both estimation methods. On the other hand, carbon balance without carbon stock, by both estimation methods, suggests that farms emit more carbon than the system is capable of stock. It was concluded that IPCC estimations can underestimate CH4 emissions from some categories while overestimate others. However, considering the whole property, these discrepancies were offset and we would submit that the equations suggested by the IPCC properly estimate the total CH4 emission and carbon balance of the properties. Thus, the IPCC equations should be utilized with caution, and the herd composition should be analyzed at the property level.