Compositional mixed modeling of methane emissions and ruminal volatile fatty acids from individual cattle and multiple experiments

Methane emissions and rumen metabolite concentrations in cattle fed two different silages

In this study, 18 animals were fed two forage-based diets: red clover (RC) and grass silage (GS), in a crossover-design experiment in which methane (CH4) emissions were recorded in respiration chambers. Rumen samples obtained through naso-gastric sampling tubes were analysed by NMR. Methane yield (g/kg DM) was significantly lower from animals fed RC (17.8 ± 3.17) compared to GS (21.2 ± 4.61) p = 0.008. In total 42 metabolites were identified, 6 showing significant differences between diets (acetate, propionate, butyrate, valerate, 3-phenylopropionate, and 2-hydroxyvalerate). Partial least squares discriminant analysis (PLS-DA) was used to assess which metabolites were more important to distinguish between diets and partial least squares (PLS) regressions were used to assess which metabolites were more strongly associated with the variation in CH4 emissions. Acetate, butyrate and propionate along with dimethylamine were important for the distinction between diets according to the PLS-DA results. PLS regression revealed that diet and dry matter intake are key factors to explain CH4 variation when included in the model. Additionally, PLS was conducted within diet, revealing that the association between metabolites and CH4 emissions can be conditioned by diet. These results provide new insights into the methylotrophic methanogenic pathway, confirming that metabolite profiles change according to diet composition, with consequences for CH4 emissions.

Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

Data describing the composition of smoke are inherently multivariate and always non-negative parts of a whole. The data are relative and the information is contained in the ratios between parts of the composition. A prior analysis of smoke emissions produced from the burning of manzanita wood mixed with low-density polyethylene plastic applied traditional statistical methods to the compositional data and found no effect. The current paper applies compositional data techniques to these smoke emissions to determine if the prior analysis was accurate. Analysis of variance of the isometric log-ratios showed that LDPE significantly affected the CO₂ emission ratio for 8 of the 191 trace gases; this analysis showed none of the gases identified in the previous analysis were affected by LDPE. LDPE did not affect the CO₂ emission ratios for the alkanes, alkenes, alkynes, aldehydes, cycloalkanes, cycloalkenes, diolefins, ketones, MAHs, and PAHs. Compositional data analysis should be used to analyze smoke emissions data. Burning contaminant-free LDPE should produce emissions like wood. Implications Reanalysis of impact of burning LDPE plastic in silvicultural debris piles using appropriate statistical techniques confirmed previously published results from inappropriate techniques that LDPE did not change the composition of the smoke emissions. Being able to dispose of these LDPE-covered forest debris by burning can save thousands of dollars in labor costs annually. Disposal of pesticide-free agricultural LDPE plastic by burning should only produce wood-like smoke emissions. This applies to LDPE/total mass ratios of 0.25- 2.5% as studied.

Nuclear Magnetic Resonance to Detect Rumen Metabolites Associated with Enteric Methane Emissions from Beef Cattle

This study presents the application of metabolomics to evaluate changes in the rumen metabolites of beef cattle fed with three different diet types: forage-rich, mixed and concentrate-rich. Rumen fluid samples were analysed by ¹H-NMR spectroscopy and the resulting spectra were used to characterise and compare metabolomic profiles between diet types and assess the potential for NMR metabolite signals to be used as proxies of methane emissions (CH₄ in g/kg DMI). The dataset available consisted of 128 measurements taken from 4 experiments with CH₄ measurements taken in respiration chambers. Predictive modelling of CH₄ was conducted by partial least squares (PLS) regression, fitting calibration models either using metabolite signals only as predictors or using metabolite signals as well as other diet and animal covariates (DMI, ME, weight, BW^0.75, DMI/BW^0.75). Cross-validated R² were 0.57 and 0.70 for the two models respectively. The cattle offered the concentrate-rich diet showed increases in alanine, valerate, propionate, glucose, tyrosine, proline and isoleucine. Lower methane yield was associated with the concentrate-rich diet (p < 0.001). The results provided new insight into the relationship between rumen metabolites, CH₄ production and diets, as well as showing that metabolites alone have an acceptable association with the variation in CH₄ production from beef cattle.

Modeling the Ecological Impact of Phosphorus in Catchments with Multiple Environmental Stressors

The broken phosphorus (P) cycle has led to widespread eutrophication of freshwaters. Despite reductions in anthropogenic nutrient inputs that have led to improvement in the chemical status of running waters, corresponding improvements in their ecological status are often not observed. We tested a novel combination of complementary statistical modeling approaches, including random-effect regression trees and compositional and ordinary linear mixed models, to examine the potential reasons for this disparity, using low-frequency regulatory data available to catchment managers. A benthic Trophic Diatom Index (TDI) was linked to potential stressors, including nutrient concentrations, soluble reactive P (SRP) loads from different sources, land cover, and catchment hydrological characteristics. Modeling suggested that SRP, traditionally considered the bioavailable component, may not be the best indicator of ecological impacts of P, as shown by a stronger and spatially more variable negative relationship between total P (TP) concentrations and TDI. Nitrate-N ( < 0.001) and TP ( = 0.002) also showed negative relationship with TDI in models where land cover was not included. Land cover had the strongest influence on the ecological response. The positive effect of seminatural land cover ( < 0.001) and negative effect of urban land cover ( = 0.030) may be related to differentiated bioavailability of P fractions in catchments with different characteristics (e.g., P loads from point vs. diffuse sources) as well as resilience factors such as hydro-morphology and habitat condition, supporting the need for further research into factors affecting this stressor-response relationship in different catchment types. Advanced statistical modeling indicated that to achieve desired ecological status, future catchment-specific mitigation should target P impacts alongside multiple stressors.

Genetic parameters of plasma and ruminal volatile fatty acids in sheep fed alfalfa pellets and genetic correlations with enteric methane emissions1

Animal-to-animal variation in methane (CH4) emissions determined in respiration chambers has a genetic basis, but rapid phenotyping methods that can be applied on-farm are required to enable increased genetic progress by the farming industry. Fermentation of carbohydrates in the rumen results in the formation of VFA with hydrogen (H2) as a byproduct that is used for CH4 formation. Generally, fermentation pathways leading to acetate are associated with the most H2 production, less H2 formation is associated with butyrate production, and propionate and valerate production are associated with reduced H2 production. Therefore, VFA may constitute a potential correlated proxy for CH4 emissions to enable high-throughput animal screening. The objective of the present study was to determine the genetic parameters for ruminal and plasma VFA concentrations in sheep fed alfalfa (Medicago sativa L.) pellets and their genetic (rg) and phenotypic (rp) correlations with CH4 emissions. Measurements of CH4 emissions in respiration chambers and ruminal (stomach tubing 18 h from last meal) and blood plasma (3 h post-feeding) VFA concentrations were made on 1,538 lambs from 5 birth years (2007 and 2009 to 2012) aged between 5 and 10 mo, while the animals were fed alfalfa pellets at 2.0 times maintenance requirements in 2 equal size meals (0900 and 1500 h). These measurements were repeated twice (rounds) 14 d apart. Mean (± SD) CH4 production was 24.4 ± 3.08 g/d, and the mean CH4 yield was 15.8 ± 1.51 g/kg DMI. Mean concentration of total ruminal VFA was 52.2 mM, with concentrations of acetate, propionate and butyrate of 35.97, 8.83, and 4.02 mM, respectively. Ruminal total VFA concentration had heritability (h2) and repeatability estimates (± SE) of 0.24 ± 0.05 and 0.35 ± 0.03, respectively, and similar estimates were found for acetate, propionate, and butyrate. Blood plasma concentrations of VFA had much lower estimates of h2 and repeatability than ruminal VFA. Genetic correlations with CH4 yield were greatest for total concentrations of ruminal VFA and acetate, with 0.54 ± 0.12 and 0.56 ± 0.12, respectively, which were much greater than their corresponding rp. The rp and rg of ruminal VFA proportions and blood VFAs with CH4 emissions were in general lower than for ruminal VFA concentrations. However, minor ruminal VFA proportions had also moderate rg with CH4 yield. Pre-feeding concentrations of total VFA and acetate were the strongest correlated proxies to select sheep that are genetically low CH4 emitters.

Effects of long-term diet supplementation with Gliricidia sepium foliage mixed with Enterolobium cyclocarpum pods on enteric methane, apparent digestibility, and rumen microbial population in crossbred heifers1

In the last decades, strategies have been evaluated to reduce rumen methane (CH4) production by supplementing tropical forages rich in secondary compounds; however, most of these beneficial effects need to be validated in terms of their persistence over time. The aim of this study was to assess CH4 emissions over time in heifers fed with and without Gliricidia sepium foliage (G) mixed with ground pods of Enterolobium cyclocarpum(E). Two groups of 4 crossbred (Bos taurus x Bos indicus) heifers (284 ±17 kg initial weight) were fed with 2 diets (0% and 15% of a mixture of the pods and foliage [E + G:0 and E + G:15, respectively]) over 80 d, plus 2 wk before the experiment, in which every animal was fed a legume and pod-free diet. Every 14 d, CH4 production, apparent digestibility, volatile fatty acids (VFA), and microbial population were quantified for each animal. The experiment was conducted with a repeated measurements design over time. Diets fed differed in terms of their crude protein (CP), condensed tannins, and saponins content supplied by E. cyclocarpum and G. sepium. For most of the experiment, dry matter intake (DMI) and digestible dry-matter intake (DDMI) were 6.3 kg DMI/d and 512 g DDMI/kg, respectively, for both diets (diet: P > 0.05). Apparent digestible crude protein (DCP) was reduced by 21 g DCP/kg DM when the diet was supplemented with E + G:15 (P = 0.040). Molar proportions of VFA's in the rumen did not differ between diets or in time (P > 0.05). Daily methane production, expressed in relation to DMI, was 23.95 vs. 23.32 g CH4/kg DMI for the diet E + G:0 and E + G:15, respectively (diet: P = 0.016; Time: P > 0.05). Percent gross energy loss as CH4 (Ym) with grass-only diets was above 8.1%, whereas when feeding heifers with the alternate supplementation, Ym values of 7.59% (P = 0.016) were observed. The relative abundance of total bacterial, protozoa, and methanogenic archaeal replicates was not affected by time nor by the incorporation of legume and pods into the diet (P > 0.05). Results suggest that addition of G. sepium mixed with E. cyclocarpum pods can reduce CH4 production in heifers and this response remains over time, without effect on microbial population and VFA concentration and a slight reduction in CPD digestibility.

Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH4), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH4 emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH4 emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH4 emissions and methanogens were the microbial populations most closely correlated with CH4 emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH4 emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH4 emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH4, but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH4. Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH4 emissions across diverse production systems and environments.