Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts based on Metagenomic Gene Abundance

Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB) were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI) were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e.g. human metabolism, health and behaviour, as well as to understand the genetic link between host and microbiome

Prediction of phosphorus output in manure and milk by lactating dairy cows.

Mathematical models for predicting P excretions play a key role in evaluating P use efficiency and monitoring the environmental impact of dairy cows. However, the majority of extant models require feed intake as predictor variable, which is not routinely available at farm level. The objectives of the study were to (1) explore factors explaining heterogeneity in P output; (2) develop a set of empirical models for predicting P output in feces (Pf), manure (PMa), and milk (Pm, all in g/cow per day) with and without dry matter intake (DMI) using literature data; and (3) evaluate new and extant P models using an independent data set. Random effect meta-regression analyses were conducted using 190 Pf, 97 PMa, and 118 Pm or milk P concentration (PMilkC) treatment means from 38 studies. Dietary nutrient composition, milk yield and composition, and days in milk were used as potential covariates to the models with and without DMI. Dietary phosphorus intake (Pi) was the major determinant of Pf and PMa. Milk yield negatively affected Pi partitioning to Pf or PMa. In the absence of DMI, milk yield, body weight, and dietary P content became the major determinants of Pf and PMa. Milk P concentration (PMilkC) was heterogeneous across the treatment groups, with a mean of 0.92 g/kg of milk. Milk yield, days in milk, and dietary Ca-to-ash ratio were negatively correlated with PMilkC and explained 42% of the heterogeneity. The new models predicted Pf and PMa with root mean square prediction error as a percentage of observed mean (RMSPE%) of 18.3 and 19.2%, respectively, using DMI when evaluated with an independent data set. Some of the extant models also predicted Pf and PMa well (RMSPE%=19.3 to 20.0%) using DMI. The new models without DMI as a variable predicted Pf and PMa with RMSPE% of 22.3 and 19.6%, respectively, which can be used in monitoring P excretions at farm level. When evaluated with an independent data set, the new model and extant models based on milk protein content predicted PMilkC with RMSPE% of 12.7 to 19.6%. Although models using P intake information gave better predictions, P output from lactating dairy cows can also be predicted well without intake using milk yield, milk protein content, body weight, and dietary P, Ca, and total ash contents

Prediction of drinking water intake by dairy cows.

Mathematical models that predict water intake by drinking, also known as free water intake (FWI), are useful in understanding water supply needed by animals on dairy farms. The majority of extant mathematical models for predicting FWI of dairy cows have been developed with data sets representing similar experimental conditions, not evaluated with modern cows, and often require dry matter intake (DMI) data, which may not be routinely available. The objectives of the study were to (1) develop a set of new empirical models for predicting FWI of lactating and dry cows with and without DMI using literature data, and (2) evaluate the new and the extant models using an independent set of FWI measurements made on modern cows. Random effect meta-regression analyses were conducted using 72 and 188 FWI treatment means with and without dietary electrolyte and daily mean ambient temperature (TMP) records, respectively, for lactating cows, and 19 FWI treatment means for dry cows. Milk yield, DMI, body weight, days in milk, dietary macro-nutrient contents, an aggregate milliequivalent concentration of dietary sodium and potassium (NaK), and TMP were used as potential covariates to the models. A model having positive relationships of DMI, dietary dry matter (DM%), and CP (CP%) contents, NaK, and TMP explained 76% of variability in FWI treatment means of lactating cows. When challenged on an independent data set (n=261), the model more accurately predicted FWI [root mean square prediction error as a percentage of average observed value (RMSPE%)=14.4%] compared with a model developed without NaK and TMP (RMSPE%=17.3%), and all extant models (RMSPE%≥15.7%). A model without DMI included positive relationships of milk yield, DM%, NaK, TMP, and days in milk, and explained 63% of variability in the FWI treatment means and performed well (RMSPE%=17.9%), when challenged on the independent data. New models for dry cows included positive relationships of DM% and TMP along with DMI or body weight. The new models with and without DMI explained 75 and 54% of the variability in FWI treatment means of dry cows and had RMSPE% of 12.8 and 15.2%, respectively, when evaluated with the literature data. The study offers a set of empirical models that can assist in determining drinking water needs of dairy farms

Quantifying body water kinetics and fecal and urinary water output from lactating Holstein dairy cows.

Reliable estimates of fresh manure water output from dairy cows help to improve storage design, enhance efficiency of land application, quantify the water footprint, and predict nutrient transformations during manure storage. The objective of the study was to construct a mechanistic, dynamic, and deterministic mathematical model to quantify urinary and fecal water outputs (kg/d) from individual lactating dairy cows. The model contained 4 body water pools: reticulorumen (QRR), post-reticulorumen (QPR), extracellular (QEC), and intracellular (QIC). Dry matter (DM) intake, dietary forage, DM, crude protein, acid detergent fiber and ash contents, milk yield, and milk fat and protein contents, days in milk, and body weight were input variables to the model. A set of linear equations was constructed to determine drinking, feed, and saliva water inputs to QRR and fractional water passage from QRR to QPR. Water transfer via the rumen wall was subjected to changes in QEC and total water input to QRR. Post-reticulorumen water passage was adjusted for DM intake. Metabolic water production and respiratory cutaneous water losses were estimated with functions of heat production in the model. Water loss in urine was driven by absorbed N left after being removed via milk. Model parameters were estimated simultaneously using observed fecal and urinary water output data from lactating Holstein cows (n=670). The model was evaluated with data that were not used for model development and optimization (n=377). The observations in both data sets were related to thermoneutral conditions. The model predicted drinking water intake, fecal, urinary, and total fresh manure water output with root mean square prediction errors as a percentage of average values of 18.1, 15.6, 30.6, and 14.6%, respectively. In all cases, >97% of the prediction error was due to random variability of data. The model can also be used to determine saliva production, heat and metabolic water production, respiratory cutaneous water losses, and size of major body water pools in lactating Holstein cows under thermoneutral conditions

Effects of phytase supplementation on phosphorus retention in broilers and layers: a meta-analysis.

Phytase, a widely used feed additive in poultry diets, increases P availability and subsequently reduces inorganic-P supplementation and P-excretion. Phytase supplementation effect on P-retention in poultry has been investigated, but the effect sizes were highly variable. The present study's objective was to conduct several meta-analyses to quantitatively summarize the phytase effect on P-retention in broilers and layers. Data from 103 and 26 controlled experiments testing the phytase effect on P-retention were included in 2 separate meta-analyses for broilers and layers, respectively. The mean difference calculated by subtracting the means of P-retention for the control group from the phytase-supplemented group was chosen as an effect size estimate. Between-study variability (heterogeneity) of mean difference was estimated using random-effect models and had a significant effect (P < 0.01) in both broilers and layers. Therefore, random-effect models were extended to mixed-effect models to explain heterogeneity and obtain final phytase effect size estimates. Available dietary and bird variables were included as fixed effects in the mixed-effect models. The final broiler mixed-effect model included phytase dose and Ca-to-total-P ratio (Ca:tP), explaining 15.6% of the heterogeneity. Other variables such as breed might further explain between-study variance. Broilers consuming control diets were associated with 48.4% P-retention. Exogenous phytase supplementation at 1,039 FTU/kg of diet increased P-retention by 8.6 percentage units on average. A unit increase of phytase dose and Ca:tP from their means further increased P-retention. For layers, the final mixed-effect models included dietary Ca, age, and experimental period length. The variables explained 65.9% of the heterogeneity. Layers receiving exogenous phytase at 371 FTU/kg were associated with a 5.02 percentage unit increase in P-retention. A unit increase in dietary Ca from its mean increased P-retention, whereas an increase in the experiment length and layer's age decreased P-retention. Phytase supplementation had a significant positive effect on P-retention in both broilers and layers, but effect sizes across studies were significantly heterogeneous due to differences in Ca contents, experiment length, bird age, and phytase dose

Impacts of dietary forage and crude protein levels on the shedding of Escherichia coli O157:H7 and Listeria in dairy cattle feces

The shedding of Escherichia coli O157:H7 and Listeria monocytogenes in the feces of ruminants and the consequential risk to the public and environmental health is well reported. However, the influence of dietary manipulation on the shedding of fecal bacteria is not well understood. This study was conducted to improve understanding of the relationship between dietary feed composition and shedding of E. coli O157:H7 and Listeria spp. in dairy feces. Twelve cows were randomly assigned to four treatment diets of two dietary forage levels: low forage (37.4% dry matter, DM) vs. high forage (53.3% of DM) and two dietary crude protein (CP) levels: low protein (15.2% of DM) vs. high protein (18.5% of DM) in a 4×4 replicated Latin square design with four periods each including a 14 d adaptation and 3 d sample collection periods. Generic E. coli was detected in some of the feed ingredients, such as cotton seed, alfalfa hay, almond, and CaCO3, while Listeria was detected in the alfalfa hay and mineral mix. A significant interaction effect was observed between dietary forage and CP on the presence of fecal E. coli O157:H7 (P=0.01) but not with Listeria. On average, the greatest E. coli O157:H7 level (6.6 log10 CFU/g of feces) was observed from the high forage and high protein diet and the lowest level was 6.1 log10 CFU/g from the low forage and high protein diet. The average Listeria shedding rate was within the range of 1.7–2.3 log10 CFU/g among the dietary forage and CP treatments. For the CP treatments, significantly low levels of Listeria were observed from cows fed the high protein (0.9−1.6 log10 CFU/g) compared to the low protein (1.3–2.1 log10 CFU/g) diet. Considering temporal fluctuations, no significant diurnal pattern was observed for either E.coli O157:H7 or Listeria. In addition, no time of sampling over day by dietary forage or CP content interaction on fecal E.coli O157:H7 or Listeria level was observed. This study showed that diets can influence the shedding of potentially pathogenic bacteria in dairy cow excreta

Exogenous β-mannanase improves feed conversion efficiency and reduces somatic cell count in dairy cattle.

Exogenous fibrolytic enzymes have been shown to be a promising way to improve feed conversion efficiency (FCE). β-Mannanase is an important enzyme digesting the polysaccharide β-mannan in hemicellulose. Supplementation of diets with β-mannanase to improve FCE has been more extensively studied in nonruminants than in ruminants. The objective of this study was to investigate the effects of β-mannanase supplementation on nutrient digestibility, FCE, and nitrogen utilization in lactating Holstein dairy cows. Twelve post-peak-lactation multiparous Holstein cows producing 45.5±6.6kg/d of milk at 116±19.0d in milk were randomly allotted to 1 of 3 treatments in a 3×3 Latin square design with 3 periods of 18d (15d for adaptation plus 3d for sample collection). All cows were fed the same basal diet and the 3 treatments differed only by the β-mannanase dose: 0% dry matter (DM; control), 0.1% of DM (low supplement, LS), and 0.2% of DM (high supplement, HS) supplemented to the basal diet. Supplementation of β-mannanase enzyme at the LS dose reduced dry matter intake (DMI) but did not affect milk yield or milk composition. Cows receiving LS produced 90g more milk per kg of DMI compared with control cows. Somatic cell count (SCC) in milk was lower for cows fed the LS diet compared with cows fed control diets. Cows fed LS diet had lower DM, organic matter and crude protein digestibility compared with cows fed control diets. Starch, neutral detergent fiber, and acid detergent fiber digestibility were not affected by LS. Milk yield, DMI, SCC, and nutrient digestibility did not change for HS. Despite the reduced crude protein digestibility, reduced N intake led to similar fecal N excretions in LS cows and control cows (234 vs. 235g/cow per day). Urinary N excretions remained similar between enzyme-fed and control cows (~190g/cow per day), although the percentage of N intake partitioned to urinary N tended to be greater in LS than in control cows (31 vs. 27%). Cows fed LS significantly improved the percentage of apparently absorbed N partitioned to milk protein N (42 vs. 38%). When supplemented at 0.1% of dietary DM, β-mannanase can improve FCE and lower the SCC of dairy cows without affecting milk yield, milk composition, or total manure N excretions of dairy cows