Relationship between rate of glucose or propionate infusion and milk protein yield and concentration in dairy cows: A meta-regression.

Although postruminal glucose infusion into dairy cows has increased milk protein yield in some past experiments, the same trend has not been observed in others. A meta-regression of 64 sets of observations from 29 previously published glucose and propionate infusion studies in dairy cattle, treating study and experiment (study) as random effects, was performed to establish the general effects of glucose equivalent (GlcE) infusion rate on milk true protein (MTP) yield and content, if any, and to identify independent, fixed-effect variables that accounted for the changes in MTP yield and content that were observed. Candidate explanatory variables included rate and site of infusion, diet composition and intake, body weight and lactation stage of the cows, and the change in nutrient intake between GlcE and control treatments. Across all studies, according to a model containing only the random effects of study and experiment, GlcE infusion at an average of 954 g/d increased MTP yield by 26 g/d, on average, whereas mean MTP content was not affected. Backward stepwise elimination of potential explanatory variables from a full mixed model produced a final, reduced model for MTP yield that retained a positive, second-order quadratic effect of infusion rate of GlcE and a positive, linear effect of the change in crude protein intake (CPI) between GlcE treatment and control. This change in CPI due to GlcE infusion ranged from -0.546 to 0.173 kg/d in the dataset. The model fit indicated that when CPI was allowed to drop during GlcE infusion, the effect of GlcE on MTP yield was smaller than when CPI was maintained or increased, in a manifestation of the classic protein:energy interaction. The final reduced model for MTP content contained the same explanatory variables as for MTP yield, plus a negative effect of intravenous compared with gastrointestinal infusion. Overall, the meta-analysis revealed that both MTP yield, and content were positively related to GlcE infusion rate and to the change in CPI between glucose treatment and control.

SNAPIG: a model to study nutrient digestion and absorption kinetics in growing pigs based on diet and ingredient properties.

Current feed formulation and evaluation practices rely on static values for the nutritional value of feed ingredients and assume additivity. Hereby, the complex interplay among nutrients in the diet and the highly dynamic digestive processes are ignored. Nutrient digestion kinetics and diet × animal interactions should be acknowledged to improve future predictions of the nutritional value of complex diets. Therefore, an in silico nutrient-based mechanistic digestion model for growing pigs was developed: "SNAPIG" (Simulating Nutrient digestion and Absorption kinetics in PIGs). Aiming to predict the rate and extent of nutrient absorption from diets varying in ingredient composition and physicochemical properties, the model represents digestion kinetics of ingested protein, starch, fat, and non-starch polysaccharides, through passage, hydrolysis, absorption, and endogenous secretions of nutrients along the stomach, proximal small intestine, distal small intestine, and caecum + colon. Input variables are nutrient intake and the physicochemical properties (i.e. solubility, and rate and extent of degradability). Data on the rate and extent of starch and protein hydrolysis of different ingredients per digestive segment were derived from in vitro assays. Passage of digesta from the stomach was modelled as a function of feed intake level, dietary nutrient solubility and diet viscosity. Model evaluation included testing against independent data from in vivo studies on nutrient appearance in (portal) blood of growing pigs. When simulating diets varying in physicochemical properties and nutrient source, SNAPIG can explain variation in glucose absorption kinetics (postprandial time of peak, TOP: 20-100 min observed vs 25-98 min predicted), and predict variation in the extent of ileal protein and fat digestion (root mean square prediction errors (RMSPE) = 12 and 16%, disturbance error = 12 and 86%, and concordance correlation coefficient = 0.34 and 0.27). For amino acid absorption, the observed variation in postprandial TOP (61 ± 11 min) was poorly predicted despite accurate mean predictions (58 ± 34 min). Recalibrating protein digestion and amino acid absorption kinetics require data on net-portal nutrient appearance, combined with observations on digestion kinetics, in pigs fed diets varying in ingredient composition. Currently, SNAPIG can be used to forecast the time and extent of nutrient digestion and absorption when simulating diets varying in ingredient and nutrient composition. It enhances our quantitative understanding of nutrient digestion kinetics and identifies knowledge gaps in this field of research. Already useful as research tool, SNAPIG can be coupled with a postabsorptive metabolism model to predict the effects of dietary and feeding-strategies on the pig's growth response.

Comparison of imputation methods for missing production data of dairy cattle.

Nowadays, vast amounts of data representing feed intake, growth, and environmental impact of individual animals are being recorded in on-farm settings. Despite their apparent use, data collected in real-world applications often have missing values in one or several variables, due to reasons including human error, machine error, or sampling frequency misalignment across multiple variables. Since incomplete datasets are less valuable for downstream data analysis, it is important to address the missing value problem properly. One option may be to reduce the dataset to a subset that contains only complete data, but considerable data may be lost via this process. The current study aimed to compare imputation methods for the estimation of missing values in a raw dataset of dairy cattle including 454 553 records collected from 629 cows between 2009 and 2020. The dataset was subjected to a cleaning process that reduced its size to 437 075 observations corresponding to 512 cows. Missing values were present in four variables: concentrate DM intake (CDMI, missing percentage = 2.30%), forage DM intake (FDMI, 8.05%), milk yield (MY, 15.12%), and BW (64.33%). After removing all missing values, the resulting dataset (n = 129 353) was randomly sampled five times to create five independent subsets that exhibit the same missing data percentages as the cleaned dataset. Four univariate and nine multivariate imputation methods (eight machine learning methods and the MissForest method) were applied and evaluated on the five repeats, and average imputation performance was reported for each repeat. The results showed that Random Forest was overall the best imputation method for this type of data and had a lower mean squared prediction error and higher concordance correlation coefficient than the other imputation methods for all imputed variables. Random Forest performed particularly well for imputing CDMI, MY, and BW, compared to imputing FDMI.

Review: When worlds collide - poultry modeling in the 'Big Data' era.

Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learning (ML) modeling approaches, with strengths in forecasting and prediction. This review explores the evolution of empirical and mechanistic models in poultry production systems, and how these models may interact with new digital tools and technologies. This review will also examine the emergence of ML and Big Data in the poultry production sector, and the emergence of precision feeding and automation of poultry production systems. There are several promising directions for the field, including: (1) application of Big Data analytics (e.g., sensor-based technologies, precision feeding systems) and ML methodologies (e.g., unsupervised and supervised learning algorithms) to feed more precisely to production targets given a 'known' individual animal, and (2) combination and hybridization of data-driven and mechanistic modeling approaches to bridge decision support with improved forecasting capabilities.

A dynamic model to measure retention of solid and liquid digesta fractions in chickens fed diets with differing fibre sources.

Dietary fibres impact multiple digestive processes, and insights into the effects of various types of fibre on digesta retention time are required to optimise current feed formulation systems. Therefore, the objective of this study was to apply a dynamic modelling approach to generate estimates for the retention time of solid and liquid digesta in broilers fed different fibre sources. A maize-wheat-soybean meal control diet was compared against three diets in which wheat was partially substituted with oat hulls, rice husks, or sugar beet pulp (3% w/w). Non-starch polysaccharide (NSP) digestibility was evaluated in broilers between 23 and 25 days of age (n = 60 birds/treatment) using titanium dioxide (TiO2, 0.5 g/kg) as a marker, after feeding the experimental diets for 21 days. Digesta mean retention time (MRT) was measured in another 108 birds at 30 days of age by the administration of an oral pulse dose of chromium sesquioxide (Cr2O3) as solid marker and Cobalt-EDTA as liquid marker, and subsequent measurement of marker recovery in compartments of the digestive tract (n = 2 or 3 replicate birds/time point/treatment). Marker recovery models to estimate fractional passage rates for solid and liquid digesta in crop, gizzard, small intestine, and caeca compartments of the gastrointestinal tract were developed to predict MRT of solid and liquid digesta for each dietary treatment. The models were composed of a series of first-order differential equations, representing the variation of marker concentration in a compartment over time. Estimated MRT of solid and liquid digesta in the gizzard varied from 20 min for oat hulls and 34 min for rice husks diets to 14 min for sugar beet pulp and 12 min for control diets. In the caeca, liquid MRT was decreased compared to the control diet (989 min) for the sugar beet pulp diet (516 min), while it was increased for both the oat hulls and rice husks diets (≈1 500 min). Overall, these estimates are greater than those previously reported, suggesting that liquid digesta retention in the caeca previously has been underestimated. Digestibility of total NSP was increased by dietary fibre inclusion, regardless of the fibre type, although degradation of constituent sugars of NSP varied among diets. In conclusion, the inclusion of fibre sources at a low level (3% w/w) in the diet of broiler modulated retention time mainly in the gizzard and caeca, and increased digestibility of NSP.

Meta-analysis of apparent ruminal synthesis and postruminal flow of B vitamins in dairy cows.

As milk production has significantly increased over the past decade(s), existing estimates of the B-vitamin needs of the modern dairy cow are currently being reconsidered, as suboptimal B-vitamin supply may affect metabolic efficiency. At the same time, however, "true" (i.e., biologically active forms, excluding nonfunctional analogs) B-vitamin supply also cannot be adequately estimated by dietary intake, as the rumen microbiota has been shown to play a significant role in synthesis and utilization of B vitamins. Given their complex impact on the metabolism of dairy cows, incorporating these key nutrients into the next generation of mathematical models could help to better predict animal production and performance. Therefore, the purpose of this study was to generate hypotheses of regulation in the absence of supplemental B vitamins by creating empirical models, through a meta-analysis, to describe true B-vitamin supply to the cow (postruminal flow, PRF) and apparent ruminal synthesis (ARS). The database used for this meta-analysis consisted of 340 individual cow observations from 15 studies with 16 experiments, where diet and postruminal digesta samples were (post hoc) analyzed for content of B vitamins (B1, B2, B3, B6, B9, B12). Equations of univariate and multivariate linear form were considered. Models describing ARS considered dry matter intake (DMI, kg/d), B-vitamin dietary concentration [mg/kg of dry matter (DM)] and rumen-level variables such as rumen digestible neutral detergent fiber (NDF) and starch (g/kg of DM), total volatile fatty acids (VFA, mM), acetate, propionate, butyrate, and valerate molar proportions (% of VFA), mean pH, and fractional rates of degradation of NDF and starch (%/h). Models describing PRF considered dietary-level driving variables such as DMI, B-vitamin dietary concentration (mg/kg of DM), starch and crude protein (g/kg of DM) and forage NDF (g/kg of DM). Equations developed were required to contain all significant slope parameters and contained no significant collinearity between driving variables. Concordance correlation coefficient was used to evaluate the models on the developmental data set due to data scarcity. Overall, modeling ARS yielded better-performing models compared with modeling PRF, and DMI was included in all prediction equations as a scalar variable. The B-vitamin dietary concentration had a negative effect on the ARS of B1, B2, B3, and B6 but increased the PRF of B2 and B9. The rumen digestible NDF concentration had a negative effect on the ARS of B2, B3, and B6, whereas rumen digestible starch concentration had a negative effect on the ARS of B1 and a positive effect on the ARS of B9. In the best prediction models, the dietary starch increased PRF of B1, B2, and B9 but decreased PRF of B12. The equations developed may be used to better understand the effect of diet and ruminal environment on the true supply of B vitamins to the dairy cow and stimulate the development of better-defined requirements in the future.

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data.

Mechanistic models (MMs) have served as causal pathway analysis and 'decision-support' tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches - access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

An isotope dilution model for partitioning of phenylalanine and tyrosine uptake by the liver of lactating dairy cows.

An isotope dilution model to describe the partitioning of phenylalanine and tyrosine in the bovine liver was developed. The model comprises four intracellular and six extracellular pools and various flows connecting these pools and external blood. Conservation of mass principles were applied to generate the fundamental equations describing the behaviour of the system in the steady state. The model was applied to datasets from multi-catheterised dairy cattle during a constant infusion of [1-13C]phenylalanine and [2,3,5,6-2H]tyrosine tracers. Model solutions described the extraction of phenylalanine and tyrosine from the liver via the portal vein and hepatic artery. In addition, the exchange of free phenylalanine and tyrosine between extracellular and intracellular pools was explained and the hydroxylation of phenylalanine to tyrosine was estimated. The model was effective in providing information about the fates of phenylalanine and tyrosine in the liver and could be used as part of a more complex system describing amino acid metabolism in the whole animal.

A mechanistic model of small intestinal starch digestion and glucose uptake in the cow.

The high contribution of postruminal starch digestion (up to 50%) to total-tract starch digestion on energy-dense, starch-rich diets demands that limitations to small intestinal starch digestion be identified. A mechanistic model of the small intestine was described and evaluated with regard to its ability to simulate observations from abomasal carbohydrate infusions in the dairy cow. The 7 state variables represent starch, oligosaccharide, glucose, and pancreatic amylase in the intestinal lumen, oligosaccharide and glucose in the unstirred water layer at the intestinal wall, and intracellular glucose of the enterocyte. Enzymatic hydrolysis of starch was modeled as a 2-stage process involving the activity of pancreatic amylase in the lumen and of oligosaccharidase at the brush border of the enterocyte confined within the unstirred water layer. The Na+-dependent glucose transport into the enterocyte was represented along with a facilitative glucose transporter 2 transport system on the basolateral membrane. The small intestine is subdivided into 3 main sections, representing the duodenum, jejunum, and ileum for parameterization. Further subsections are defined between which continual digesta flow is represented. The model predicted nonstructural carbohydrate disappearance in the small intestine for cattle unadapted to duodenal infusion with a coefficient of determination of 0.92 and a root mean square prediction error of 25.4%. Simulation of glucose disappearance for mature Holstein heifers adapted to various levels of duodenal glucose infusion yielded a coefficient of determination of 0.81 and a root mean square prediction error of 38.6%. Analysis of model behavior identified limitations to the efficiency of small intestinal starch digestion with high levels of duodenal starch flow. Limitations to individual processes, particularly starch digestion in the proximal section of the intestine, can create asynchrony between starch hydrolysis and glucose uptake capacity.

Prediction of portal and hepatic blood flow from intake level data in cattle.

Interest is growing in developing integrated postabsorptive metabolism models for dairy cattle. An integral part of linking a multi-organ postabsorptive model is the prediction of nutrient fluxes between organs, and thus blood flow. The purpose of this paper was to use a multivariate meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF=HEPBF - PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. Both databases had information on dry matter intake (DMI), metabolizable energy intake (MEI), body weight, and a basic description of the diet including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg of BW0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg of BW0.75/d) were examined with linear and quadratic fits. Equations were developed using cow within experiment and experiment as random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation database, equations based on DMI typically resulted in lower root mean square prediction errors, expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient (CCC) values than equations based on MEI. Quadratic equation terms were frequently nonsignificant, and the quadratic equations did not outperform their linear counterparts. The best performing blood flow equations were PORBF (L/h)=202 (±45.6) + 83.6 (±3.11) × DMI (kg/d) and HEPBF (L/h)=186 (±45.4) + 103.8 (±3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted - observed) for PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h)=210 (±51.0) + 82.9 (±6.43) × forage (kg of DM/d) + 82.9 (±6.04) × concentrate (kg of DM/d), and HEPBF (L/h)=184 (±50.6) + 92.6 (±6.28) × forage (kg of DM/d) + 114.2 (±5.88) × concentrate (kg of DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian information criterion value for PORBF and HEPBF (Bayesian information criterion=6,512 vs. 7,303), as well as slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow well and can easily be used within a postabsorptive model of nutrient metabolism. Results also suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting for this difference resulted in improved ARTBF predictions.

Effects of lactic acid bacteria silage inoculation on methane emission and productivity of Holstein Friesian dairy cattle.

Inoculants of lactic acid bacteria (LAB) are used to improve silage quality and prevent spoilage via increased production of lactic acid and other organic acids and a rapid decline in silage pH. The addition of LAB inoculants to silage has been associated with increases in silage digestibility, dry matter intake (DMI), and milk yield. Given the potential change in silage and rumen fermentation conditions accompanying these silage additives, the aim of this study was to investigate the effect of LAB silage inoculants on DMI, digestibility, milk yield, milk composition, and methane (CH4) production from dairy cows in vivo. Eight mid-lactation Holstein-Friesian dairy cows were grouped into 2 blocks of 4 cows (multiparous and primiparous) and used in a 4×4 double Latin square design with 21-d periods. Methane emissions were measured by indirect calorimetry. Treatments were grass silage (mainly ryegrass) with no inoculant (GS), with a long-term inoculant (applied at harvest; GS+L), with a short-term inoculant (applied 16h before feeding; GS+S), or with both long and short-term inoculants (GS+L+S). All diets consisted of grass silage and concentrate (75:25 on a dry matter basis). The long-term inoculant consisted of a 10:20:70 mixture of Lactobacillus plantarum, Lactococcus lactis, and Lactobacillus buchneri, and the short-term inoculant was a preparation of Lc. lactis. Dry matter intake was not affected by long-term or short-term silage inoculation, nor was dietary neutral detergent fiber or fat digestibility, or N or energy balance. Milk composition (except milk urea) and fat and protein-corrected milk yield were not affected by long- or short-term silage inoculation, nor was milk microbial count. However, milk yield tended to be greater with long-term silage inoculation. Methane expressed in units of grams per day, grams per kilogram of DMI, grams per kilogram of milk, or grams per kilogram of fat and protein-corrected milk yield was not affected by long- or short-term silage inoculation. However, CH4 expressed in units of kilojoules per kilogram of metabolic body weight per day tended to be greater with long-term silage inoculation. Results of this study indicate minimal responses in animal performance to both long- and short-term inoculation of grass silage with LAB. Strain and dose differences as well as different basal silages and ensiling conditions are likely responsible for the lack of significant effects observed here, although positive effects have been observed in other studies.

Synthesis and Assay of SIRT1-Activating Compounds.

The NAD(+)-dependent deacetylase SIRT1 plays key roles in numerous cellular processes including DNA repair, gene transcription, cell differentiation, and metabolism. Overexpression of SIRT1 protects against a number of age-related diseases including diabetes, cancer, and Alzheimer's disease. Moreover, overexpression of SIRT1 in the murine brain extends lifespan. A number of small-molecule sirtuin-activating compounds (STACs) that increase SIRT1 activity in vitro and in cells have been developed. While the mechanism for how these compounds act on SIRT1 was once controversial, it is becoming increasingly clear that they directly interact with SIRT1 and enhance its activity through an allosteric mechanism. Here, we present detailed chemical syntheses for four STACs, each from a distinct structural class. Also, we provide a general protocol for purifying active SIRT1 enzyme and outline two complementary enzymatic assays for characterizing the effects of STACs and similar compounds on SIRT1 activity.

Net energy and protein requirements for pregnancy differ between goats and sheep.

Current feeding systems for goats estimate the energy and protein requirements for pregnancy using data from sheep. The objective of this study was to predict the NE and net protein requirements for pregnancy in goats carrying single and twin fetuses and to compare these requirements with those of sheep. Data were compiled from 2 studies with dairy goats and 3 studies with sheep. These studies measured the energy content (EC) and protein content (PC) of the gravid uterus and of the mammary gland using the comparative slaughter technique. The current study was performed as a meta-analysis using an exponential model, comparing species (sheep versus goats) and litter size (single versus twin) from 50 to 140 d of pregnancy. Total EC and total PC in the gravid uterus were similar in goats and sheep carrying a single fetus. Energy and protein contents of the gravid uterus of sheep carrying twins were, on average, 29% greater than that of goats with twins from 80 to 140 d of pregnancy. During pregnancy, EC and PC of the mammary gland in goats carrying singles and twins were, on average, greater than those of sheep by 9 and 24%, respectively, for EC and by 25% for PC for both litter sizes. In conclusion, the gravid uterus and the mammary gland of goats and sheep require different amounts of energy and protein. Sheep carrying twins have the greatest daily NE and net protein requirements for pregnancy followed by goats carrying twins and both species carrying a single fetus. Therefore, it is inappropriate to adopt data from sheep to predict the net pregnancy requirements of goats, and the results found in this study could be relevant to the nutritional management of dairy goats.

Effects of dietary starch content and rate of fermentation on methane production in lactating dairy cows.

The objective of this study was to investigate the effects of starch varying in rate of fermentation and level of inclusion in the diet in exchange for fiber on methane (CH4) production of dairy cows. Forty Holstein-Friesian lactating dairy cows of which 16 were rumen cannulated were grouped in 10 blocks of 4 cows each. Cows received diets consisting of 60% grass silage and 40% concentrate (dry matter basis). Cows within block were randomly assigned to 1 of 4 different diets composed of concentrates that varied in rate of starch fermentation [slowly (S) vs. rapidly (R) rumen fermentable; native vs. gelatinized corn grain] and level of starch (low vs. high; 270 vs. 530g/kg of concentrate dry matter). Results of rumen in situ incubations confirmed that the fractional rate of degradation of starch was higher for R than S starch. Effective rumen degradability of organic matter was higher for high than low starch and also higher for R than S starch. Increased level of starch, but not starch fermentability, decreased dry matter intake and daily CH4 production. Milk yield (mean 24.0±1.02kg/d), milk fat content (mean 5.05±0.16%), and milk protein content (mean 3.64±0.05%) did not differ between diets. Methane expressed per kilogram of fat- and protein-corrected milk, per kilogram of dry matter intake, or as a fraction of gross energy intake did not differ between diets. Methane expressed per kilogram of estimated rumen-fermentable organic matter (eRFOM) was higher for S than R starch-based diets (47.4 vs. 42.6g/kg of eRFOM) and for low than high starch-based diets (46.9 vs. 43.1g/kg of eRFOM). Apparent total-tract digestibility of neutral detergent fiber and crude protein were not affected by diets, but starch digestibility was higher for diets based on R starch (97.2%) compared with S starch (95.5%). Both total volatile fatty acid concentration (109.2 vs. 97.5mM) and propionate proportion (16.5 vs. 15.8mol/100mol) were higher for R starch- compared with S starch-based diets but unaffected by the level of starch. Total N excretion in feces plus urine and N retained were unaffected by dietary treatments, and similarly energy intake and output of energy in milk expressed per unit of metabolic body weight were not affected by treatments. In conclusion, an increased rate of starch fermentation and increased level of starch in the diet of dairy cattle reduced CH4 produced per unit of eRFOM but did not affect CH4 production per unit of feed dry matter intake or per unit of milk produced.

An isotope dilution model for partitioning phenylalanine and tyrosine uptake by the mammary gland of lactating dairy cows.

An isotope dilution model for partitioning phenylalanine and tyrosine uptake by the mammary gland of the lactating dairy cow is constructed and solved in the steady state. The model contains four intracellular and four extracellular pools and conservation of mass principles are applied to generate the fundamental equations describing the behaviour of the system. The experimental measurements required for model solution are milk secretion and plasma flow rate across the gland in combination with phenylalanine and tyrosine concentrations and plateau isotopic enrichments in arterial and venous plasma and free and protein bound milk during a constant infusion of [1-(13)C]phenylalanine and [2,3,5,6-(2)H]tyrosine tracer. If assumptions are made, model solution enables determination of steady state flows for phenylalanine and tyrosine inflow to the gland, outflow from it and bypass, and flows representing the synthesis and degradation of constitutive protein and phenylalanine hydroxylation. The model is effective in providing information about the fates of phenylalanine and tyrosine in the mammary gland and could be used as part of a more complex system describing amino acid metabolism in the whole ruminant.

Evaluation of the SF6 tracer technique for estimating methane emission rates with reference to dairy cows using a mechanistic model.

A dynamic, mechanistic model of the sulfur hexafluoride (SF6) tracer technique, used for estimating methane (CH4) emission rates from ruminants, was constructed to evaluate the accuracy of the technique. The model consists of six state variables and six zero-pools representing the quantities of SF6 and CH4 in rumen and hindgut fluid, in rumen and hindgut headspace, and in blood and collection canister. The model simulates flows of CH4 and SF6 through the body, subsequent eructation and exhalation and accumulation in a collection canister. The model predicts CH4 emission by multiplying the SF6 release rate of a permeation device in the rumen by the ratio of CH4:SF6 in collected air. This prediction is compared with the actual CH4 production rate, assumed to be continuous and used as a driving variable in the model. A sensitivity analysis was conducted to evaluate the effect of changes in several parameters. The predicted CH4 emission appeared sensitive to parameters affected by the difference in CH4:SF6 ratio in exhaled and eructed air respectively, viz., hindgut fractional passage rate and hindgut CH4 production. This is caused by the difference in solubility of CH4 and SF6 and by hindgut CH4 production. In addition, the predicted CH4 emission rate appeared sensitive to factors that affect proportions of exhaled and eructed air sampled, i.e., eructation time fraction, exhalation time fraction, and distance from sampling point to mouth/nostrils. Changes in rumen fractional passage rate, eructation rate, SF6 release rate, background values and air sampling rate did not noticeably affect the predicted CH4 emission. Simulations with (13)CH4 as an alternative tracer show that the differences and sensitivity to parameters greatly disappear. The model is considered a useful tool to evaluate critical points in the SF6 technique. Data from in vivo experiments are needed to further evaluate model simulations.

A dynamic mechanistic model of lactic acid metabolism in the rumen.

Current feed evaluation systems for ruminants are too imprecise to describe diets in terms of their acidosis risk. The dynamic mechanistic model described herein arises from the integration of a lactic acid (La) metabolism module into an extant model of whole-rumen function. The model was evaluated using published data from cows and sheep fed a range of diets or infused with various doses of La. The model performed well in simulating peak rumen La concentrations (coefficient of determination = 0.96; root mean square prediction error = 16.96% of observed mean), although frequency of sampling for the published data prevented a comprehensive comparison of prediction of time to peak La accumulation. The model showed a tendency for increased La accumulation following feeding of diets rich in nonstructural carbohydrates, although less-soluble starch sources such as corn tended to limit rumen La concentration. Simulated La absorption from the rumen remained low throughout the feeding cycle. The competition between bacteria and protozoa for rumen La suggests a variable contribution of protozoa to total La utilization. However, the model was unable to simulate the effects of defaunation on rumen La metabolism, indicating a need for a more detailed description of protozoal metabolism. The model could form the basis of a feed evaluation system with regard to rumen La metabolism.

Meta-analysis of factors that affect the utilization efficiency of phosphorus in lactating dairy cows.

A meta-analysis investigation based on literature data was conducted to estimate the effect size of nutritional and animal factors on phosphorus (P) excretion in feces and concentrations of P in milk. Two data sets were created for statistical analysis: One to derive prediction equations for P in feces (25 studies; 130 treatments) and another for P in milk (19 studies; 94 treatments). Prediction equations were derived using mixed model regression analysis with a random effect for study, and equations were evaluated based on values for Bayesian information criterion (BIC), root mean square prediction error (RMSPE), and concordance correlation coefficient (CCC) statistics. In terms of RMSPE and CCC values, fecal P excretion was best predicted by P intake, where P in feces (g/d)=-3.8(±3.45) + 0.64(±0.038) × P intake (g/d) (RMSPE: 18.3%, CCC: 0.869). However, significant effects of crude protein [g/kg of dry matter (DM)], neutral detergent fiber (g/kg of DM), and milk yield (kg/d) on fecal P excretion were also found. Despite a lack of improvement in terms of RMSPE and CCC values, these parameters may still explain part of the variation in fecal P excretion. For milk P, expressed as a fraction of P intake, the following equation had the highest CCC and the lowest RMSPE value: P in milk as a fraction of P intake (g/g)=0.42(±0.065) + 0.23(±0.018) × feed efficiency (i.e., fat- and protein-corrected milk yield/dry matter intake) - 0.11(±0.0199) × P in feed (g/kg of DM) (RMSPE: 19.7%; CCC: 0.761). Equations derived to predict fecal P as a fraction of P intake (g/g) or milk P content (g/kg) could not adequately explain the observed variation and did not perform well in terms of RMSPE and CCC values. Examination of the residuals showed that P balance was a seemingly confounding factor in some of the models. The results presented here can be used to estimate P in feces and milk based on commonly measured dietary and milk variables, but could also be used to guide development of mechanistic models on P metabolism in lactating dairy cattle. Factors to consider in future research and modeling efforts regarding efficiency of P use include the effects of dietary neutral detergent fiber, crude protein, starch, variation in P content of milk, and effects of P resorption from bone and body tissues during early lactation.

Quantifying the effect of monensin dose on the rumen volatile fatty acid profile in high-grain-fed beef cattle.

Monensin is a common feed additive used in various countries, where 1 of the associated benefits for use in beef cattle is improved efficiency of energy metabolism by the rumen bacteria, the animal, or both. Modeling fermentation-altering supplements is of interest, and thus, it is the purpose of this paper to quantify the change in VFA profile caused by monensin dose in high-grain-fed beef cattle. The developmental database used for meta-analysis included 58 treatment means from 16 studies from the published literature, and the proportional change in molar acetate, propionate, and butyrate (mol/100 mol) as well as total VFA (mM) with monensin feeding dose (mg/kg DM, concentration in the feed) was evaluated using the MIXED procedure (SAS Inst. Inc., Cary, NC) with the study treated as a random effect. The mean monensin dose in the literature database was 30.9 ± 3.70 mg/kg DM and ranged from 0.0 to 88.0 mg/kg DM. Mean DMI was 7.8 ± 0.26 kg DM/d, mean concentrate proportion of the diet was 0.87 ± 0.01, and mean treatment period was 42 ± 5.6 d. Results produced the following equations: proportional change in acetate (mol/100 mol) = -0.0634 (± 0.0323) × monensin (mg/kg DM)/100 (P = 0.068), proportional change in propionate (mol/100 mol) = 0.260 (± 0.0735) × monensin (mg/kg DM)/100 (P = 0.003), and proportional change in butyrate (mol/100 mol) = -0.335 (± 0.0916) × monensin (mg/kg DM)/100 (P = 0.002). The change in total VFA was not significantly related to monensin dose (P = 0.93). The results presented here indicate that the shift in VFA profile may be dose dependent, with increasing propionate and decreasing acetate and butyrate proportions (mol/100 mol). These equations could be applied within mechanistic models of rumen fermentation to represent the effect of monensin dose on the VFA profile in high-grain-fed beef cattle.