Effects of two types of Coccomyxa sp. KJ on in vitro ruminal fermentation, methane production, and the rumen microbiota.

Coccomyxa sp. KJ is a unicellular green microalga that accumulates abundant lipids when cultured under nitrogen-deficient conditions (KJ1) and high nitrogen levels when cultured under nitrogen-sufficient conditions (KJ2). Considering the different characteristics between KJ1 and KJ2, they are expected to have different effects on rumen fermentation. This study aimed to determine the effects of KJ1 and KJ2 on in vitro ruminal fermentation, digestibility, CH4 production, and the ruminal microbiome as corn silage substrate condition. Five treatments were evaluated: substrate only (CON) and CON + 0.5% dry matter (DM) KJ1 (KJ1_L), 1.0% DM KJ1 (KJ1_H), 0.5% DM KJ2 (KJ2_L), and 1.0% DM KJ2 (KJ2_H). DM degradability-adjusted CH4 production was inhibited by 48.4 and 40.8% in KJ2_L and KJ2_H, respectively, compared with CON. The proportion of propionate was higher in the KJ1 treatments than the CON treatment and showed further increases in the KJ2 treatments. The abundances of Megasphaera, Succiniclasticum, Selenomonas, and Ruminobacter, which are related to propionate production, were higher in KJ2_H than in CON. The results suggested that the rumen microbiome was modified by the addition of 0.5-1.0% DM KJ1 and KJ2, resulting in increased propionate and reduced CH4 production. In particular, the KJ2 treatments inhibited ruminal CH4 production more than the KJ1 treatments. These findings provide important information for inhibiting ruminal CH4 emissions, which is essential for increasing animal productivity and sustaining livestock production under future population growth.

Non-conductive silicon-containing materials improve methane production by pure cultures of methanogens.

Conductive materials (CM) enhance methanogenesis, but there is no clear correlation between conductivity and faster methane production (MP) rates. We investigated if MP by pure cultures of methanogens (Methanobacterium formicicum, Methanospirillum hungatei, Methanothrix harundinacea and Methanosarcina barkeri) is affected by CM (activated carbon (AC), magnetite), and other sustainable alternatives (sand and glass beads, without conductivity, and zeolites (Zeo)). The significant impact of the materials was on M. formicicum as MP was significantly accelerated by non-CM (e.g., sand reduced the lag phase (LP) duration by 48 %), Zeo and AC (LP reduction in 71% and 75 %, respectively). Conductivity was not correlated with LP reduction. Instead, silicon content in the materials was inversely correlated with the time required for complete MP, and silicon per se stimulated M. formicicum's activity. These findings highlight the potential of using non-CM silicon-containing materials in anaerobic digesters to accelerate methanogenesis.

Comparison of the Effects of Essential Oils from Cannabis sativa and Cannabis indica on Selected Bacteria, Rumen Fermentation, and Methane Production-In Vitro Study.

This study aimed to evaluate the effects of essential oils (EOs) extracted from Cannabis sativa L. and Cannabis indica Lam. on in vitro ruminal fermentation characteristics, selected rumen microbial populations, and methane production. GC-MS analyses allowed us to identify 89 compounds in both EOs. It was found that E-ß-caryophyllene predominated in C. sativa (18.4%) and C. indica (24.1%). An in vitro (Ankom) test was performed to analyse the control and monensin groups, as well as the 50 µL or 100 µL EOs. The samples for volatile fatty acids (VFAs), lactate, and microbiological analysis were taken before incubation and after 6 and 24 h. The application of EOs of C. indica resulted in an increase in the total VFAs of acetate and propionate after 6 h of incubation. The applied EOs had a greater impact on the reduction in methane production after 6 h, but no apparent effect was noted after 24 h. Lower concentrations of C. sativa and C. indica had a more pronounced effect on Lactobacillus spp. and Buryrivibrio spp. than monensin. The presented findings suggest that C. sativa and C. indica supplementation can modify ruminal fermentation, the concentrations of specific volatile fatty acids, and methane production.

Invited review: Advances in nutrition and feed additives to mitigate enteric methane emissions.

Methane, both enteric and from manure management, is the most important greenhouse gas from ruminant livestock, and its mitigation can deliver substantial decreases in the carbon footprint of animal products and potentially contribute to climate change mitigation. Although choices may be limited, certain feeding-related practices can substantially decrease livestock enteric CH4 emission. These practices can be generally classified into 2 categories: diet manipulation and feed additives. Within the first category, selection of forages and increasing forage digestibility are likely to decrease enteric CH4 emission, but the size of the effect, relative to current forage practices in the United States dairy industry, is likely to be minimal to moderate. An opportunity also exists to decrease enteric CH4 emissions by increasing dietary starch concentration, but interventions have to be weighed against potential decreases in milk fat yield and farm profitability. A similar conclusion can be made about dietary lipids and oilseeds, which are proven to decrease CH4 emission but can also have a negative effect on rumen fermentation, feed intake, and milk production and composition. Sufficient and robust scientific evidence indicates that some feed additives, specifically the CH4 inhibitor 3-nitrooxypropanol, can substantially reduce CH4 emissions from dairy and beef cattle. However, the long-term effects and external factors affecting the efficacy of the inhibitor need to be further studied. The practicality of mass-application of other mitigation practices with proven short-term efficacy (i.e., macroalgae) is currently unknown. One area that needs more research is how nutritional mitigation practices (both diet manipulation and feed additives) interact with each other and whether there is synergism among feed additives with different mode of action. Further, effects of diet on manure composition and greenhouse gas emissions during storage (e.g., emission trade-offs) have not been adequately studied. Overall, if currently available mitigation practices prove to deliver consistent results and novel, potent, and safe strategies are discovered and are practical, nutrition alone can deliver up to 60% reduction in enteric CH4 emissions from dairy farms in the United States.

Review of equations to predict methane emissions in dairy cows from milk fatty acid profiles and their application to commercial dairy farms.

Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 ± 2.26 g/kg DMI, 7 equations), methane intensity (16.05 ± 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 ± 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 ± 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.

The interplay of soil physicochemical properties, methanogenic diversity, and abundance governs methane production potential in paddy soil subjected to multi-decadal straw incorporation.

Straw incorporation holds significant promise for enhancing soil fertility and mitigating air pollution stemming from straw burning. However, this practice concurrently elevates the production and emission of methane (CH4) from paddy ecosystems. Despite its environmental impact, the precise mechanisms behind the heightened CH4 production resulting from long-term straw incorporation remain elusive. In a 32-year field experiment featuring three fertilization treatments (CFS-chemical fertilizer with wheat straw, CF-chemical fertilizer, and CK-unamended), we investigated the impact of abiotic (soil physicochemical properties) and biotic (methanogenic abundance, diversity, and community composition) factors on CH4 production in paddy fields. Results revealed a significantly higher CH4 production potential under CFS treatment compared to CF and CK treatments. The partial least squares path model revealed that soil physicochemical properties (path coefficient = 0.61), methanogenic diversity (path coefficient = -0.43), and methanogenic abundance (path coefficient = 0.29) collectively determined CH4 production potential, explaining 77% of the variance. Enhanced soil organic carbon content and water content, resulting from straw incorporation, emerged as pivotal factors positively correlated with CH4 production potential. Under CFS treatment, lower Shannon index of methanogens, compared to CF and CK treatments, was attributed to increased Methanosarcina. Notably, the Shannon index and relative abundance of Methanosarcina exhibited negative and positive correlations with CH4 production potential, respectively. Methanogenic abundance, bolstered by straw incorporation, significantly amplified overall potential. This comprehensive analysis underscores the joint influence of abiotic and biotic factors in regulating CH4 production potential during multi-decadal straw incorporation.

Bulking agent in dry anaerobic digestion as a key factor for the enhancement of biogas production.

Dry anaerobic digestion (dry-AD) is an attractive process for solid wastes such as agri-food waste. However, some limitations mainly associated to lack of effective mixing, can hinder the methane production capacity of the systems. Bulking agent (BA) has been proposed as a solution to the compaction issues in systems without mechanical agitation, such as leaching bed reactors. However, effects of BA are still not clear, and, thus, the factors to consider for its dose has not been optimized yet. This work studies the effect of BA in dry-AD. Two substrates with different characteristics were proposed as models, bean peel as a lignocellulosic substrate and a mixture of food waste as a readily biodegradable substrate. Inert plastic rings were used as BA at different BA:S ratios. Assessed BA:S ratio did not affect the performance of methane production for the lignocellulosic waste, but it did significantly affect to the easily biodegradable substrate, showing up to a 28% of methane production increase. This result could be due to the presence of lignocellulosic compounds in the bean peel, behaving like a natural BA. In assays with an increased bed height, the compaction of the system was more severe, resulting in the rapid acidification of the processes. At these conditions, the positive effect of BA addition was more marked, allowing methane production and no acidification of the system. Thus, the addition of BA is a suitable strategy for improving methane production or stability in dry-AD systems without requiring the stirring of the systems.

Distinctive effects of graphene oxide and reduced graphene oxide on methane production kinetics and pharmaceuticals removal in anaerobic reactors.

Graphene oxide (GO) addition to anaerobic digestion has been suggested to enhance direct electron transfer. The impact of GO (0.075 g GO g-1 VS) and biologically and hydrothermally reduced GO (bio-rGO and h-rGO, respectively) on the methane production kinetics and removal of 12 pharmaceuticals was assessed in Fed-batch reactors. A decrease of 15 % in methane production was observed in the tests with GO addition compared with the control and the h-rGO. However, bio-rGO and h-rGO substantially increased the methane production rate compared to the control tests (+40 %), in the third fed-batch test. Removal of pharmaceuticals was enhanced only during the bio-reduction of GO (1st fed-batch test), whereas once the GO was bio-reduced, it followed a similar trend in the control and h-rGO tests. The addition of GO can enhance the methane production rate and, therefore, reduce the anaerobic treatment time.

Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset.

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "sniffer technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Effects of 3-nitrooxypropanol (Bovaer10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions.

The objective of this study was to determine the potential effect and interaction of 3-nitrooxypropanol (3-NOP; Bovaer, DSM-Firmenich Nutrition Products Ltd.) and whole cottonseed (WCS) on lactational performance and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS; 224 ± 36 DIM, 26 ± 3.7 kg milk yield, mean ± SD), were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin square design with 2 × 2 factorial arrangements of treatments with four 24-d periods. The experimental treatments were as follows: (1) control (basal TMR), (2) 3-NOP (60 mg/kg TMR DM), (3) WCS (5% TMR DM), and (4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed System, C-Lock Inc.) during the last 3 d of each 24-d experimental period when animals were housed in tiestalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. No interaction of 3-NOP and WCS occurred for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13%, 14%, and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18% vs. 8%), CH4 intensity (g/kg milk yield; 19% vs. 3%), and CH4 intensity (g/kg ECM; 19% vs. 4%). Hydrogen production was increased by 2.85-fold in HF and 1.53-fold in BS cows receiving 3-NOP. Further, a 3-NOP × time interaction occurred for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at approximately 4 h after morning feeding, but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding, and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high-fiber diet resulted in 18% to 19% reduction in enteric CH4 emission in Swiss HF cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to the low number of cows per breed. Finally, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance the CH4 inhibition effect of 3-NOP of dairy cows.

Synergistic effect of two bacterial strains promoting anaerobic digestion of rice straw to produce methane.

A large amount of agricultural waste causes global environmental pollution. Biogas production by microbial pretreatment is an important way to utilize agricultural waste resources. In this study, Sporocytophaga CG-1 (A, cellulolytic strain) was co-cultured with Bacillus clausii HP-1 (B, non-cellulolytic strain) to analyze the effect of pretreatment of rice straw on methanogenic capacity of anaerobic digestion (AD). The results showed that weight loss rate of filter paper of co-culture combination is 53.38%, which is 29.37% higher than that of A. The synergistic effect of B on A can promote its degradation of cellulose. The cumulative methane production rate of the co-culture combination was the highest (93.04 mL/g VS substrate), which was significantly higher than that of A, B and the control group (82.38, 67.28 and 67.70 mL/g VS substrate). Auxiliary bacteria can improve cellulose degradation rate by promoting secondary product metabolism. These results provide data support for the application of co-culture strategies in the field of anaerobic digestion practices.

ß-Dicarbonyls Facilitate Engineered Microbial Bromoform Biosynthesis.

Ruminant livestock produce around 24% of global anthropogenic methane emissions. Methanogenesis in the animal rumen is significantly inhibited by bromoform, which is abundant in seaweeds of the genus Asparagopsis. This has prompted the development of livestock feed additives based on Asparagopsis to mitigate methane emissions, although this approach alone is unlikely to satisfy global demand. Here we engineer a non-native biosynthesis pathway to produce bromoform in vivo with yeast as an alternative biological source that may enable sustainable, scalable production of bromoform by fermentation. ß-dicarbonyl compounds with low pKa values were identified as essential substrates for bromoform production and enabled bromoform synthesis in engineered Saccharomyces cerevisiae expressing a vanadate-dependent haloperoxidase gene. In addition to providing a potential route to the sustainable biological production of bromoform at scale, this work advances the development of novel microbial biosynthetic pathways for halogenation.

Dietary concentrate supplementation increases milk production and reduces predicted greenhouse gas emission intensity in pasture-based commercial dairy farms.

Controlled studies have extensively documented that concentrate supplements typically increase enteric CH4 emissions and milk yield and reduce emissions per unit of milk produced and dry matter intake. However, no studies have been conducted to determine the effect of concentrate on predicted greenhouse gas emissions from dairy farms representing the Australian pasture-based farming system. Thus, this study sought to determine how dietary concentrate supplementation affects enteric and manure CH4, and N2O of Australian pasture-based dairy farms. The Australian Dairy Carbon Calculator was used, which incorporates emission factors and methodologies used in the National Greenhouse Gas Inventory as reported to the Intergovernmental Panel on Climate Change. Primary data were collected and analyzed from 120 commercial farms in Australia's major dairy regions. Then the farms were divided into 4 groups based on their dietary concentrate supplementation: ≤1 (low; 15 farms), 1 to 2 (moderate; 35 farms), 2 to 3 (high; 35 farms), and ≥3 (very high; 35 farms) t of concentrate dry matter (tDM) per cow per year. Sources of greenhouse gas emissions were CO2 from concentrate production, enteric CH4, and manure CH4 and N2O. Total dry matter intake, milk yield, and daily enteric CH4 production (g/d) quadratically increased with concentrate level, whereas greenhouse gas emission intensity of milk production (kg of CO2 equivalent per kg of fat- and protein-corrected milk) decreased by 14% for farms supplementing with ≥3 tDM/cow per year compared with those supplementing with ≤1 tDM/cow per year of dietary concentrate. The N2O and CH4 emissions from manure increased quadratically and linearly, respectively, with the increasing supplementation of concentrate. Farms supplementing 2 to 3 tDM/cow per year showed substantial increases in gross income, gross margin, earnings before interest and tax, and net income ($/cow per year) compared with those supplementing of ≤1, 1 to 2, and ≥3 tDM/cow per year. Overall, increasing dietary concentrate supplementation for dairy cows resulted in increased milk production per cow, reduced greenhouse gas emissions per unit of milk produced, and increased income and profit. However, a comprehensive life cycle assessment study is needed to account for carbon sequestration by other farm components, such as pastures and trees, which were not considered in the present study. In addition, the present study was based on modeling and did not gather ground truth information for DMI, digestibility, crude protein, and urinary and fecal N excretion. Therefore, data should be interpreted with caution, and studies gathering such information are encouraged.

Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first-lactation Holstein cows.

The large-scale recording of traits such as feed efficiency (FE) and methane emissions (ME) for use in genetic improvement programs is complex, costly, and time-consuming. Therefore, heritable traits that can be continuously recorded in dairy herds and are correlated with FE and ME traits could provide useful information for genetic evaluation. Rumination time has been suggested to be associated with FE, methane production (MeP; ME in g/d), and production traits at the phenotypic level. Therefore, the objective of this study was to investigate the genetic relationships among rumination time (RT), FE, methane and production traits using 7,358 records from 656 first-lactation Holstein cows. The estimated heritabilities were moderate for RT (0.45 ± 0.14), MeP (0.36 ± 0.12), milk yield (0.40 ± 0.08), fat yield (0.29 ± 0.06), protein yield (0.32 ± 0.07), and energy-corrected milk (0.28 ± 0.07), but were low and nonsignificant for FE (0.15 ± 0.07), which was defined as the residual of the multiple linear regression of DMI on energy-corrected milk and metabolic body weight. A favorable negative genetic correlation was estimated between RT and MeP (-0.53 ± 0.24), whereas a positive favorable correlation was estimated between RT and energy-corrected milk (0.49 ± 0.11). The estimated genetic correlation of RT with FE (-0.01 ± 0.17) was not significantly different from zero but showed a trend of a low correlation with dry matter intake (0.21 ± 0.13). These results indicate that RT is genetically associated with MeP and milk production traits, but high standard errors indicate that further analyses should be conducted to verify these findings when more data for RT, MeP, and FE become available.

Replacing soybean meal with microalgae biomass in diets with contrasting carbohydrate profiles can reduce in vitro methane production and improve short-chain fatty acid production.

The objective of this study was to evaluate the interaction of dietary carbohydrate profile and soybean meal (SBM) replacement with either Chlorella pyrenoidosa (CHL) or Spirulina platensis (SPI) on in vitro fermentation. This experiment was conducted as a randomized complete block design, with fermentation run (3 runs) considered as blocks. The treatments were arranged in a 2 × 5 factorial design, where the first factor was the carbohydrate profile, which was composed of diets containing 42.5% neutral detergent fiber (NDF) and 26.8% starch (HF-LS) or 26.8% NDF and 40.6% starch (LF-HS), and the second factor was the protein source, in which a control diet (100% SBM), partial replacement of SBM with CHL (1/2CHL) or SPI (1/2SPI), or total replacement of SBM with CHL or SPI were used. All experimental diets were formulated to have 17% crude protein. The ruminal fluid was collected from 2 lactating Holstein cows, buffered with Van Soest medium at a ratio of 1:2 and added to serum bottles containing 0.50 g of the experimental diets. Bottles were incubated at 39°C for 24 and 48 h in triplicate; headspace pressure was measured, along with gas collection for methane (CH4) quantification at 0, 2, 4, 8, 16, 24, 36, and 48 h after incubation. The final medium was used to measure pH, ammonia, and VFA. After incubation, feed bags were recovered and used for estimation of dry matter (DM), NDF, and organic matter (OM) degradability. Statistical analysis was carried out using the MIXED procedure of SAS, with carbohydrate profile, protein source, assay, and their interactions as fixed effects, with run and bottle as random effects. Orthogonal contrasts were used to compare carbohydrate profile, algae species, carbohydrate profile × algae interaction, and linear and quadratic effects of SBM replacement with CHL or SPI. There was no interaction effect between carbohydrate profile and algae source. The LF-HS improved gas production, degradability of nutrients, and VFA, mainly increasing the production of butyrate and propionate. When compared with CHL, SPI had a greater degradability of nutrients and branched VFA, along with reduction in total gas production and tended to reduce total CH4 yield. The replacement of SBM with algae linearly reduced the degradability of nutrients, along with a linear reduction in gas production. When replacement of SBM with only SPI was evaluated, SPI slightly reduced the degradability of nutrients; however, it promoted a linear reduction in CH4 yield, as well as reduction in CH4 yield by unit of degraded DM, NDF, and OM. In summary, there was no interaction of carbohydrate profile and protein source, which means that SBM replacement had a similar effect, regardless of dietary carbohydrate profile. Spirulina may be a more suitable algae source than Chlorella due to the potential to reduce CH4.

Long-term effects of 3-nitrooxypropanol on methane emission and milk production characteristics in Holstein-Friesian dairy cows.

The objective was to determine the long-term effect of 3-nitrooxypropanol (3-NOP) on CH4 emission and milk production characteristics from dairy cows receiving 3-NOP in their diet for a full year, covering all lactation stages of the dairy cows. Sixty-four late-lactation Holstein-Friesian cows (34% primiparous) were blocked in pairs, based on expected calving date, parity, and daily milk yield. The experiment started with an adaptation period of 1 wk followed by a covariate period of 3 wk in which all cows received the same basal diet and baseline measurements were performed. Directly after, cows within a block were randomly allocated to 1 of 2 dietary treatments: a diet containing on average 69.8 mg 3-NOP/kg DM (total ration level, corrected for intake of nonsupplemented GreenFeed bait) and a diet containing a placebo. Forage composition as well as forage-to-concentrate ratio altered with lactation stage (i.e., dry period and early, mid, and late lactation). Diets were provided as a total mixed ration, and additional bait was fed in GreenFeed units (C-Lock Inc.), which were used for emission measurements. Supplementation of 3-NOP did not affect total DMI, BW, or BCS, but resulted in a 6.5% increase in the yields of energy-corrected milk and fat- and protein-corrected milk (FPCM). Furthermore, milk fat and protein as well as feed efficiency were increased upon 3-NOP supplementation. Overall, a reduction of 21%, 20%, and 27% was achieved for CH4 production (g/d), yield (g/kg DMI), and intensity (g/kg FPCM), respectively, upon 3-NOP supplementation. The CH4 mitigation potential of 3-NOP was affected by the lactation stage dependent diet to which 3-NOP was supplemented. On average, a 16%, 20%, 16%, and 26% reduction in CH4 yield (g/kg DMI) was achieved upon 3-NOP supplementation for the dry period, and early, mid, and late-lactation diets, respectively. The CH4 mitigation potential of 3-NOP was affected by the length of 3-NOP supplementation within a lactation stage dependent diet and by variation in diet composition within a lactation stage dependent diet as a result of changes in grass and corn silage silos. In conclusion, 3-NOP reduced CH4 emission from cows receiving 3-NOP for a year, with a positive effect on production characteristics. The CH4 mitigation potential of 3-NOP was influenced by diet type, diet composition, and nutrition value, and the efficacy of 3-NOP appeared to decline over time but not continuously. Associated with changes in diet composition, increased efficacy of 3-NOP was observed at the start of the trial, at the start of a new lactation, and, importantly, at the end of the trial. These results suggest that diet composition has a large effect on the efficacy of 3-NOP, perhaps even larger than the week of supplementation after first introduction of 3-NOP. More studies are needed to clarify the long-term effects of 3-NOP on CH4 emission and to further investigate what influence variation in diet composition may have on the mitigation potential of 3-NOP.

How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the emission of greenhouse gases from anthropogenic sources. Recent studies have shown that the selection of lower CH4-emitting cows is possible, but doing so would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emissions from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A dataset with 3 experiments and a total of 149 lactating dairy cows with repeated measures was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed (C-Lock, Rapid City, SD). The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53 when only performance measures were used, and to 0.66 when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure because the latter simplifies the model and avoids over-fitting. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

Effects of volatile fatty acids in biohydrogen effluent on biohythane production from palm oil mill effluent under thermophilic condition

Background: Biohydrogen effluent contains a high concentration of volatile fatty acid (VFA) mainly as butyric, acetic, lactic and propionic acids. The presence of various VFAs (mixture VFAs) and their cooperative effects on two-stage biohythane production need to be further studied. The effect of VFA concentrations in biohydrogen effluent of palm oil mill effluent (POME) on methane yield in methane stage of biohythane production was investigated. Results: The methane yield obtained in low VFA loading (0.9 and 1.8 g/L) was 15­20% times greater than that of high VFA loading (3.6 and 4.7 g/L). Butyric acid at high concentrations (8 g/L) has the individual significantly negative effect the methane production process (P b 0.05). Lactic, acetic and butyric acid mixed with propionic acid at a concentration higher than 0.5 g/L has an interaction significantly negative effect on the methanogenesis process (P b 0.05). Inhibition condition had a negative effect on both bacteria and archaea with inhibited on Geobacillus sp., Thermoanaerobacterium thermosaccharolyticum, Methanoculleus thermophilus and Methanothermobacter delfuvii resulting in low methane yield. Conclusion: Preventing the high concentration of butyric acid, and propionic acid in the hydrogenic effluent could enhance methane production in two-stage anaerobic digestion for biohythane production.

Biomethane production from vinasse in upflow anaerobic sludge blanket reactors inoculated with granular sludge

ABSTRACT The main objective of this study was to evaluate the anaerobic conversion of vinasse into biomethane with gradual increase in organic loading rate (OLR) in two upflow anaerobic sludge blanket (UASB) reactors, R1 and R2, with volumes of 40.5 and 21.5 L in the mesophilic temperature range. The UASB reactors were operated for 230 days with a hydraulic detection time (HDT) of 2.8 d (R1) and 2.8-1.8 d (R2). The OLR values applied in the reactors were 0.2-7.5 g totalCOD (L d)−1 in R1 and 0.2-11.5 g totalCOD (L d)−1 in R2. The average total chemical oxygen demand (totalCOD) removal efficiencies ranged from 49% to 82% and the average conversion efficiencies of the removed totalCOD into methane were 48-58% in R1 and 39-65% in R2. The effluent recirculation was used for an OLR above 6 g totalCOD (L d)−1 in R1 and 8 gtotalCOD (L d)−1 in R2 and was able to maintain the pH of the influent in R1 and R2 in the range from 6.5 to 6.8. However, this caused a decrease for 53-39% in the conversion efficiency of the removed totalCOD into methane in R2 because of the increase in the recalcitrant COD in the influent. The largest methane yield values were 0.181 and 0.185 (L) CH4 (gtotal COD removed)−1 in R1 and R2, respectively. These values were attained after 140 days of operation with an OLR of 5.0-7.5 g totalCOD (L d)−1 and total COD removal efficiencies around 70 and 80%.

Residuos agroindustriales con potencial para la producción de metano mediante la digestión anaerobia / Agroindustrial wastes methanization and bacterial composition in anaerobic digestion

Las toneladas de residuos orgánicos que se generan anualmente en la agroindustria pueden aprovecharse como materia prima para la producción de metano. Para que los residuos orgánicos se puedan convertir a metano a gran escala, es importante que previamente se realicen sobre ellos pruebas de biodegradabilidad; un parámetro importante que conviene establecer es su potencial bioquímico de metano. En el presente trabajo se estudió la biodegradabilidad, la producción de metano y el comportamiento de poblaciones de eubacterias y arqueobacterias durante la digestión anaerobia de residuos de plátano, mango y papaya provenientes de la agroindustria, adicionando un inóculo microbiano. Los residuos de mango y plátano tenían mayor contenido de materia orgánica (94 y 75 %, respectivamente) que el residuo de papaya con base en su relación sólidos volátiles/sólidos totales. Después de 63 días de tratamiento, la mayor producción de metano se observó en la digestión anaerobia del residuo de plátano: 63,89 ml de metano por g de demanda química de oxígeno del residuo. Los resultados del potencial bioquímico de metano demostraron que el residuo de plátano tiene el mejor potencial para ser usado como materia prima en la producción de metano. A través de un análisis por PCR-DGGE con oligonucleótidos específicos se logró evaluar el tamaño y la composición de las poblaciones de eubacterias y arqueobacterias presentes en la digestión anaerobia de residuos agroindustriales a lo largo del proceso.

The tons of organic waste that are annually generated by agro-industry, can be used as raw material for methane production. For this reason, it is important to previously perform biodegradability tests to organic wastes for their full scale methanization. This paper addresses biodegradability, methane production and the behavior of populations of eubacteria and archaeabacteria during anaerobic digestion of banana, mango and papaya agroindustrial wastes. Mango and banana wastes had higher organic matter content than papaya in terms of their volatile solids and total solid rate (94 and 75 % respectively). After 63 days of treatment, the highest methane production was observed in banana waste anaerobic digestion: 63.89 ml CH4/per gram of chemical oxygen demand of the waste. In the PCR-DGGE molecular analysis, different genomic footprints with oligonucleotides for eubacteria and archeobacteria were found. Biochemical methane potential results proved that banana wastes have the best potential to be used as raw material for methane production. The result of a PCR- DGGE analysis using specific oligonucleotides enabled to identify the behavior of populations of eubacteria and archaeabacteria present during the anaerobic digestion of agroindustrial wastes throughout the process.