The objective of this study was to investigate whether the CH₄ mitigation potential of 3-nitrooxypropanol (3-NOP) in dairy cattle was affected by basal diet (BD) composition. The experiment involved 64 Holstein-Friesian dairy cows (146 ± 45 d in milk at the start of trial; mean ± SD) in 2 overlapping crossover trials, each consisting of 2 measurement periods. Cows were blocked according to parity, d in milk, and milk yield, and randomly allocated to 1 of 3 diets: a grass silage-based diet (GS) consisting of 30% concentrates and 70% grass silage (DM basis), a grass silage- and corn silage-mixed diet (GSCS) consisting of 30% concentrates, 42% grass silage, and 28% corn silage (DM basis), or a corn silage-based diet (CS) consisting of 30% concentrates, 14% grass silage, and 56% corn silage (DM basis). Two types of concentrates were formulated, viz. a concentrate for the GS diet and a concentrate for the CS diet, to meet the energy and protein requirements for maintenance and milk production. The concentrate for the GSCS diet consisted of a 50:50 mixture of both concentrates. Subsequently, the cows within each type of BD received 2 treatments in a crossover design: either 60 mg of 3-NOP/kg of DM (NOP60) and a placebo with 0 mg of 3-NOP/kg of DM (NOP0) in one crossover or 80 mg of 3-NOP/kg of DM (NOP80) and NOP0 in the other crossover. Diets were provided as total mixed ration in feed bins, which automatically recorded feed intake. Additional concentrate was fed in the GreenFeed system that was used to measure emissions of CH₄ and H₂. The CS diets resulted in a reduced CH₄ yield (g/kg DMI) and CH₄ intensity (g/kg milk). Feeding 3-NOP resulted in a decreased DMI. Milk production and composition did not differ between NOP60 and NOP0, whereas milk yield and the yield of major components decreased for NOP80 compared with NOP0. Feed efficiency was not affected by feeding 3-NOP. Interactions between BD and supplementation of 3-NOP were observed for the production (g/d) and yield (g/kg DMI) of both CH₄ and H₂, indicating that the mitigating effect of 3-NOP depended on the composition of the BD. Emissions of CH₄ decreased upon 3-NOP supplementation for all BD, but the decrease in CH₄ emissions was smaller for GS (-26.2% for NOP60 and -28.4% for NOP80 in CH₄ yield) compared with both GSCS (-35.1% for NOP60 and -37.9% for NOP80 for CH₄ yield) and CS (-34.8% for NOP60 and -41.6% for NOP80 for CH₄ yield), with no difference between the latter 2 BD. Emissions of H₂ increased upon 3-NOP supplementation for all BD, but the H₂ yield (g/kg DMI) increased 3.16 and 3.30-fold, respectively, when NOP60 and NOP80 were supplemented to GS, and 4.70 and 4.96 fold, respectively, when NOP60 and NOP80 were supplemented to CS. In conclusion, 3-NOP can effectively decrease CH₄ emissions in dairy cows across diets, but the level of CH₄ mitigation is greater when supplemented in a corn silage-based diet compared with a grass silage-based diet.

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 week followed by a covariate period of 3 weeks 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 non-supplemented 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 which were used for emission measurements. Supplementation of 3-NOP did not affect total dry matter intake (DMI), body weight or body condition score, 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 diet, 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 impact 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 suggests 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. Further studies are needed to clarify the long-term effect of 3-NOP on CH₄ emission and to further investigate the effect that variation in diet composition may have on the mitigation potential of 3-NOP.

Rumen sensors provide specific information to help understand rumen functioning in relation to health disorders and to assist in decision-making for farm management. This review focuses on the use of rumen sensors to measure ruminal pH and discusses variation in pH in both time and location, pH-associated disorders and data analysis methods to summarize and interpret rumen pH data. Discussion on the use of rumen sensors to measure redox potential as an indication of the fermentation processes is also included. Acids may accumulate and reduce ruminal pH if acid removal from the rumen and rumen buffering cannot keep pace with their production. The complexity of the factors involved, combined with the interactions between the rumen and the host that ultimately determine ruminal pH, results in large variation among animals in their pH response to dietary or other changes. Although ruminal pH and pH dynamics only partially explain the typical symptoms of acidosis, it remains a main indicator and may assist to optimize rumen function. Rumen pH sensors allow continuous monitoring of pH and of diurnal variation in pH in individual animals. Substantial drift of non-retrievable rumen pH sensors, and the difficulty to calibrate these sensors, limits their application. Significant within-day variation in ruminal pH is frequently observed, and large distinct differences in pH between locations in the rumen occur. The magnitude of pH differences between locations appears to be diet dependent. Universal application of fixed conversion factors to correct for absolute pH differences between locations should be avoided. Rumen sensors provide high-resolution kinetics of pH and a vast amount of data. Commonly reported pH characteristics include mean and minimum pH, but these do not properly reflect severity of pH depression. The area under the pH × time curve integrates both duration and extent of pH depression. The use of this characteristic, as well as summarizing parameters obtained from fitting equations to cumulative pH data, is recommended to identify pH variation in relation to acidosis. Some rumen sensors can also measure the redox potential. This measurement helps to understand rumen functioning, as the redox potential of rumen fluid directly reflects the microbial intracellular redox balance status and impacts fermentative activity of rumen microorganisms. Taken together, proper assessment and interpretation of data generated by rumen sensors requires consideration of their limitations under various conditions.

Livestock feed encompasses both human edible and human inedible components. Human edible feed components may become less available for livestock. Especially for proteins, this calls for action. This review focuses on using alternative protein sources in feed and protein efficiency, the expected problems, and how these problems could be solved. Breeding for higher protein efficiency leading to less use of the protein sources may be one strategy. Replacing (part of) the human edible feed components with human inedible components may be another strategy, which could be combined with breeding for livestock that can efficiently digest novel protein feed sources. The potential use of novel protein sources is discussed. We discuss the present knowledge on novel protein sources, including the consequences for animal performance and production costs, and make recommendations for the use and optimization of novel protein sources (1) to improve our knowledge on the inclusion of human inedible protein into the diet of livestock, (2) because cooperation between animal breeders and nutritionists is needed to share knowledge and combine expertise, and (3) to investigate the effect of animal-specific digestibility of protein sources for selective breeding for each protein source and for precision feeding. Nutrigenetics and nutrigenomics will be important tools.

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.