Carbohydrates (e.g., starch and cellulose) are the main energy source in the diets of dairy cows. The ruminal digestion of starch and cellulose is achieved by microorganisms and digestive enzymes. In order to improve their digestibility, the microbes and enzymes involved in starch and cellulose degradation should be identified and their role(s) and activity known. As existing and new analytical techniques are continuously being developed, our knowledge of the amylolytic and cellulolytic microbial community in the rumen of dairy cows has been evolving rapidly. Using traditional culture-based methods, the main amylolytic and cellulolytic bacteria, fungi and protozoa in the rumen of dairy cows have been isolated. These culturable microbes have been found to only account for a small fraction of the total population of microorganisms present in the rumen. A more recent application of the culture-independent approach of metagenomics has acquired a more complete genetic structure and functional composition of the rumen microbial community. Metagenomics can be divided into functional metagenomics and sequencing-based computational metagenomics. Both approaches have been applied in determining the microbial composition and function in the rumen. With these approaches, novel microbial species as well as enzymes, especially glycosyl hydrolases, have been discovered. This review summarizes the current state of knowledge regarding the major amylolytic and cellulolytic microorganisms present in the rumen of dairy cows. The ruminal amylases and cellulases are briefly discussed. The application of metagenomics technology in investigating glycosyl hydrolases is provided and the novel enzymes are compared in terms of glycosyl hydrolase families related to amylolytic and cellulolytic activities.

Background: Equine gut microbiology studies to date have primarily focused on horses and ponies, which represent only one of the eight extant equine species. This is despite asses and mules comprising almost half of the world’s domesticated equines, and donkeys being superior to horses/ponies in their ability to degrade dietary fiber. Limited attention has also been given to commensal anaerobic fungi and archaea even though anaerobic fungi are potent fiber degrading organisms, the activity of which is enhanced by methanogenic archaea. Therefore, the objective of this study was to broaden the current knowledge of bacterial, anaerobic fungal and archaeal diversity of the equine fecal microbiota to multiple species of equines. Core taxa shared by all the equine fecal samples (n = 70) were determined and an overview given of the microbiota across different equine types (horse, donkey, horse × donkey and zebra).Results: Equine type was associated with differences in both fecal microbial concentrations and community composition. Donkey was generally most distinct from the other equine types, with horse and zebra not differing. Despite this, a common bacterial core of eight OTUs (out of 2070) and 16 genus level groupings (out of 231) was found in all the fecal samples. This bacterial core represented a much larger proportion of the equine fecal microbiota than previously reported, primarily due to the detection of predominant core taxa belonging to the phylaKiritimatiellaeota(formerlyVerrucomicrobiasubdivision 5) andSpirochaetes. The majority of the core bacterial taxa lack cultured representation. Archaea and anaerobic fungi were present in all animals, however, no core taxon was detected for either despite several taxa being prevalent and predominant.Conclusions: Whilst differences were observed between equine types, a core fecal microbiota existed across all the equines. This core was composed primarily of a few predominant bacterial taxa, the majority of which are novel and lack cultured representation. The lack of microbial cultures representing the predominant taxa needs to be addressed, as their availability is essential to gain fundamental knowledge of the microbial functions that underpin the equine hindgut ecosystem.

Background: Compared to horses and ponies, donkeys have increased degradation of dietary fiber. The longer total mean retention time of feed in the donkey gut has been proposed to be the basis of this, because of the increased time available for feed to be acted upon by enzymes and the gut microbiota. However, differences in terms of microbial concentrations and/or community composition in the hindgut may also underpin the increased degradation of fiber in donkeys. Therefore, a study was conducted to assess if differences existed between the fecal microbiota of pony, donkey and hybrids derived from them (i.e. pony × donkey) when fed the same forage diet.Results: Fecal community composition of prokaryotes and anaerobic fungi significantly differed between equine types. The relative abundance of two bacterial genera was significantly higher in donkey compared to both pony and pony x donkey:Lachnoclostridium10 and ‘probable genus 10’ from theLachnospiraceaefamily. The relative abundance ofPiromyceswas significantly lower in donkey compared to pony × donkey, with pony not significantly differing from either of the other equine types. In contrast, the uncultivated genus SK3 was only found in donkey (4 of the 8 animals). The number of anaerobic fungal OTUs was also significantly higher in donkey than in the other two equine types, with no significant differences found between pony and pony × donkey. Equine types did not significantly differ with respect to prokaryotic alpha diversity, fecal dry matter content or fecal concentrations of bacteria, archaea and anaerobic fungi.Conclusions: Donkey fecal microbiota differed from that of both pony and pony × donkey. These differences related to a higher relative abundance and diversity of taxa with known, or speculated, roles in plant material degradation. These findings are consistent with the previously reported increased fiber degradation in donkeys compared to ponies, and suggest that the hindgut microbiota plays a role. This offers novel opportunities for pony and pony × donkey to extract more energy from dietary fiber via microbial mediated strategies. This could potentially decrease the need for energy dense feeds which are a risk factor for gut-mediated disease.

The current study has two objectives: (1) To determine the effect of different lactic acid bacteria (LAB) strains’ inoculant on silage quality of fresh ryegrass (FR) and rain-treated ryegrass (RTR), and (2) to find the optimal way (silage inoculant vs. direct-fed microbial (DFM)) to use LAB strains in order to improve nutrient digestibility and reduce methane emission (CH₄) in ruminant production. Five LAB strains were tested, Lactiplantibacillus plantarum AGR-1, L. plantarum AGR-2, Lactococcus lactis subsp. lactis biovar diacetylactis AGR-3, L. lactis subsp. lactis AGR-4 and L. lactis subsp. lactis AGR-5. Each LAB strain was inoculated at 10⁶ cfu/g fresh weight into the FR and the RTR and ensiled for 60 days. After ensiling, the effect of LAB strains included as a DFM or silage inoculant on rumen digestibility and CH₄ production were measured using an in vitro gas production system with three separate runs. The in vitro experiment consisted of 24 treatments (2 grasses (FR and RTR) × 2 ways (inoculant or DFM) × 6 strains (5 LAB strains + 1 Control)). The results indicated that the LAB strains’ inoculant treatments reduced (p < 0.0001) the dry matter (DM) losses, the NH₃ concentration (p < 0.0001) and the pH (p = 0.0019) upon ensiling in both the FR and the RTR. The lowest values in dry matter (DM) loss and NH₃ concentration were found in the L. plantarum (AGR-2) and L. lactis (AGR-5). The in vitro CH₄ production was lower for silages inoculated with L. plantarum (AGR-1, p = 0.0054), L. lactis (AGR-4, p = 0.026), L. lactis (AGR-5, p = 0.029) and L. plantarum (AGR-2, p = 0.090), compared to the control. Methane production was lower (p = 0.0027) for LABs when used as silage inoculants, compared to being used as DFM. Lactic acid bacteria used as silage inoculants increased (p ≤ 0.0001) the in vitro DM and organic matter (OM) degradability both in the FR and the RTR, whereas LAB strains used as DFM showed no such effect. The DM and OM digestibility were highest in the L. plantarum (AGR-1, p = 0.0175). Among the five LAB strains used in the current study, L. plantarum (AGR-2) was the best candidate to improve silage quality. Our observations suggest that these LAB strains are most promising when used as silage inoculants and to be confirmed in vivo.

The dairy sector contributes to global warming through enteric methane (CH4) emissions. Methane is also a loss of energy to the ruminant. Several studies have developed CH4 prediction models to assess mitigation strategies to reduce emissions. However, the majority of these models have low predictive ability or require numerous inputs that are often not readily available in commercial dairy operations. In this context, the objective of the present paper was to develop CH4 prediction models by using varying levels of information available at the farm level. The seven complexity levels used the following information: (1) dietary nutrient composition, (2) milk yield and composition, (3) Levels 1 and 2, (4) Level 3 plus dry matter intake (DMI), (5) Level 4 plus bodyweight, (6) Level 2 plus DMI, and (7) DMI only. Models were fitted to 489 individual enteric-CH4 measurements from 30 indirect calorimetry studies and evaluated with an independent database comprising 215 treatment means from 62 studies collected from the literature. Within each complexity level, all possible mixed-effect models were fitted and those with the lowest values of Akaike or Bayesian information criteria were selected using lme4 package in R. Models were evaluated using mean square prediction error (MSPE) based statistic, root MSPE (RMSPE) to observation standard deviation ratio, concordance correlation coefficient and Nash–Sutcliffe efficiency methods. All fitted models performed well with an acceptable error estimates (RMSPE as a percentage of observed mean (RMSPE%) = 16–24%), with more than two-thirds of total error originating from random bias. Overall, models with DMI were more accurate (RMSPE% = 16–20%) than those without (RMSPE% = 20–24%). Although the best prediction model (RMSPE% = 16%) was developed using Level 5 information, a model using Level 2 information is recommended for on-farm methane estimates if DMI is not measured. The proposed models offer easy and practical tools to dairy producers for predicting CH4 emissions and evaluating CH4 mitigation strategies.