Seaweeds contain a myriad of nutrients and bioactives including proteins, carbohydrates and to a lesser extent lipids as well as small molecules including peptides, saponins, alkaloids and pigments. The bioactive bromoform found in the red seaweed Asparagopsis taxiformis has been identified as an agent that can reduce enteric CH₄ production from livestock significantly. However, sustainable supply of this seaweed is a problem and there are some concerns over its sustainable production and potential negative environmental impacts on the ozone layer and the health impacts of bromoform. This review collates information on seaweeds and seaweed bioactives and the documented impact on CH₄ emissions in vitro and in vivo as well as associated environmental, economic and health impacts.

In response to oral application, monensin alters the rumen microbiota, increasing ruminal propionate production and energy availability in the animal. Data from different studies indicate that the susceptibility of rumen bacteria to monensin is mainly cell-wall dependent but tracing its activity to specific microbial groups has been challenging. Several studies have shown a similar effect for essential oils but results are inconsistent. To investigate the influence of monensin and a blend of essential oils (BEO, containing thymol, guaiacol, eugenol, vanillin, salicylaldehyde, and limonene) on the rumen microbiome, rumen liquid samples were collected orally on d 56 postpartum from cows that had either received a monensin controlled-release capsule 3 wk antepartum, a diet containing a BEO from 3 wk antepartum onward, or a control diet (n = 12). The samples were analyzed for pH, volatile fatty acid, ammonia, and lipopolysaccharide concentrations and protozoal counts. A 16S rRNA gene fingerprinting analysis (PCR-single-strand conformation polymorphism) and sequencing revealed that the BEO treatment had no effect on the rumen microbiota, whereas monensin decreased bacterial diversity. Twenty-three bacterial species-level operational taxonomic units were identified for which monensin caused a significant decrease in their relative abundance, all belonging to the phyla Bacteroidetes (uncultured BS11 gut group and BS9 gut group) and Firmicutes (Lachnospiraceae, Ruminococcaceae, and Erysipelotrichaceae). Ten bacterial operational taxonomic units belonging to the phyla Actinobacteria (Coriobacteriaceae), Bacteroidetes (Prevotella), Cyanobacteria (SHA-109), and Firmicutes (Lachnospiraceae and Ruminococcaceae) increased in relative abundance due to the monensin treatment. These results confirm the hypothesis that varying effects depending on cell-wall constitution and thickness might apply for monensin sensitivity rather than a clear-cut difference between gram-negative and gram-positive bacteria. No effect of monensin on the archaea population was observed, confirming the assumption that reported inhibition of methanogenesis is most likely caused through a decrease in substrate availability, rather than by a direct effect on the methanogens. The data support the hypothesis that the observed increase in ruminal molar propionate proportions due to monensin may be caused by a decrease in abundance of non-producers and moderate producers of propionate and an increase in abundance of succinate and propionate producers.

Different studies have shown a strong correlation between the rumen microbiome and a range of production traits (e.g., feed efficiency, milk yield and components) in dairy cows. Underlying dynamics concerning cause and effect are, however, still widely unknown and warrant further investigation. The aim of the current study was to describe possible functional interrelations and pathways using a large set of variables describing the production, the metabolic and immunological state, as well as the rumen microbiome and fermentation characteristics of dairy cows in early lactation (n = 36, 56 ± 3 d in milk). It was further hypothesized that the feed intake-associated behavior may influence the ruminal fermentation pattern, and a set of variables describing these individual animal attributes was included. Principal component analysis as well as Spearman's rank correlations were conducted including a total of 265 variables. The attained plots describe several well-known associations between metabolic, immunological, and production traits. Main drivers of variance within the data set included milk production and efficiency as well as rumen fermentation and microbiome diversity attributes, whereas behavioral, metabolic, and immunological variables did not exhibit any strong interrelations with the other variables. The previously well-documented strong correlation of production traits with distinct prokaryote groups was confirmed. This mainly included a negative correlation of operational taxonomic units ascribed to the Prevotella genus with milk and fat yield and feed efficiency. A central role of the animals' feed intake behavior in this context could not be affirmed. Furthermore, different methodological and interpretability aspects concerning the microbiome analysis by 16S rRNA gene sequencing, such as the discrepancy between taxonomic classification and functional communality, as well as the comparability with other studies, are discussed. We concluded that, to further investigate the driving force that causes the difference between efficient and inefficient animals, studies including more sophisticated methods to describe phenotypical traits of the host (e.g., rumen physiology, metabolic and genetic aspects) as well as the rumen microbiome (e.g., metagenome, metatranscriptome, metaproteome, and metabolome analysis) are needed.

The present period is characterized by a growing world population and a higher demand for more and better quality food, as well as other products for an improved standard of living. In the future, there will be increasingly strong competition for arable land and non-renewable resources such as fossil carbon-sources, water, and some minerals, as well as between food, feed, fuel, fiber, flowers, and fun (6 F’s). Proteins of animal origin like milk, meat, fish, eggs and, probably, insects are very valuable sources of essential amino acids, minerals and vitamins, but their production consumes some non-renewable resources including arable land and causes considerable emissions. Therefore, this study´s objective was to calculate some examples of the land use (arable land and grassland) for production of edible animal protein taking into consideration important animal species/categories, levels of plant and animal yields, the latter estimated with and without co-products from agriculture, and the food/biofuel industry in animal feeding. There are large differences between animal species/categories and their potential to produce edible protein depending on many influencing variables. The highest amounts per kilogram body weight are produced by growing broiler chicken followed by laying hens and dairy cows; the lowest yields in edible protein and the highest land need were observed for beef cattle. This review clearly indicates that the production of food of animal origin is a very complex process, and selective considerations, i.e., focusing on single factors, do not provide an assessment that reflects the complexity of the subject.

In spring dairy cows are often gradually transitioned from a silage- and concentrate-based ration (total mixed ration, TMR) to pasture. Rumen microbiota adaptability is a key feature of ruminant survival strategy. However, only little is known on the temporal and spatial microbial alterations involved. This study aims to investigate how the rumen liquid (LAAB), particle (PAAB) and epithelium (EAAB) associated archaea and bacteria are influenced by this nutritional change. A 10-wk trial was performed, including 10 rumen-fistulated dairy cows, equally divided into a pasture- and a confinement- group (PG and CG). The CG stayed on a TMR-based ration, while the PG was gradually transitioned from TMR to pasture (wk 1: TMR-only, wk 2: 3 h/day on pasture, wk 3 & 4: 12 h/day on pasture, wk 5-10: pasture-only). In wk 1, wk 5 and wk 10 samples of solid and liquid rumen contents, and papillae biopsies were collected. The DNA was isolated, and PCR-SSCP and 16S rRNA gene amplicon sequencing analysis were performed. Cluster analysis revealed a higher similarity between LAAB and PAAB, compared to the EAAB, characterized by higher species diversity. At all three locations the microbiota was significantly influenced by the ration change, opposite the generally acknowledged hypothesis that the EAAB remain more consistent throughout dietary changes. Even though the animals in the PG were already on a full-grazing ration for 4-6 days in wk 5, the microbiota at all three locations was significantly different compared to wk 10, suggesting an adaptation period of several days to weeks. This is in line with observations made on animal level, showing a required time for adaptation of 2-3 weeks for production and metabolic variables. A large part of the rumen prokaryote species remained unaltered upon transition to pasture and exhibited a strong host influence, supporting the hypothesis that the rumen microbiota consists of a core and a variable microbiota. For the effect of the location as well as the ration change either very similar or opposite trends among member species of common taxa were observed, demonstrating that microbes that are phylogenetically close may still exhibit substantially different phenotypes and functions.

The objective of the present study was to validate the accuracy of algorithms, implemented in the currently available RumiWatch Converter (RWC) version V0.7.4.5 of the RumiWatch System (RWS), for the classification of behavioural characteristics from jaw and head movements which are monitored by a noseband halter comprising a pressure sensor and a triaxial accelerometer. The accurate classification of behavioural characteristics in different time resolutions is critical for the usage of the RWS for scientific and practical purposes as chewing behaviour provides essential indicators for the assessment of diet adequacy in dairy cows. To validate the RWC V0.7.4.5 classification accuracy for behavioural characteristics of rumination, eating, drinking, other activity and ruminating chews per bolus by direct observation as reference method, 14 dairy cows participated in the trial. Concordance between the consolidated 1-min and 1-h classification results was assessed. The RWC V0.7.4.5 classified only rumination and ruminating chews per bolus precisely, whereas an algorithm optimisation for the classification of eating, drinking and other activity is required. Additionally, classification results from the 1-min and 1-h time summaries were not in agreement with each other except for rumination.