Variation in urea kinetics associated with ruminant species, dietary characteristics, and ruminal fermentation: A meta-analysis.

The objective of this meta-analysis was to quantitatively summarize variations in urea kinetics related to ruminant species, diet composition, and ruminal fermentation. A database of 31 studies measuring urea recycling kinetics were used to derive 2 sets of linear mixed-effects regression models. Study was used as a random intercept and regressions were weighted by 1 divided by the standard error of the mean observation. Models were compared, when appropriated, using the concordance correlation coefficient, root estimated variance associated with study (σˆs) and error (σˆe) and corrected Akaike information criterion values. From a dietary standpoint, most response variables were affected by measures reflecting dietary crude protein [(CP; e.g., N-NH3 or rumen-degradable protein (RDP)] and by variables reflecting dietary energy content [e.g., total digestible nutrients (TDN), dietary starch, or ruminal pH]. Dietary CP, N-NH3, and TDN typically had positive slopes on urea N entry rate (UER; g/d and g/kg0.75), whereas starch and TDN/RDP had negative slopes on UER (g/kg0.75). On the other hand, increasing TDN increased gastrointestinal entry rate (GER; g/kg0.75), whereas an opposite effect was observed for RDP. Increasing diet RDP content reduced the urea N returned to ornithine cycle (ROC; g/kg0.75) in most models. Ruminal variables also reflected the importance of N and energy supplies. Ruminal ammonia concentration significantly affected ROC (g/d and g/kg0.75), used for anabolism (UUA; g/kg0.75), ROC:GER, UUA:GER, and the incorporation of recycled urea N into microbial N relative to gastrointestinal entry rate of urea. Ruminal pH significantly affected GER:UER and ROC:GER ratios. Total digestible nutrients had a positive slope on UUA (g/kg0.75). Increasing the ratio of energy to protein (TDN:RDP) increased the GER:UER ratio, decreased the ROC:GER ratio, and increased the UUA:GER ratio and the incorporation of recycled urea N into microbial N relative to gastrointestinal entry rate of urea N. Comparison among models revealed that species was an important explanatory variable affecting most response variables. However, whether these differences are related to the intrinsic N metabolism of each species or due to the diet variation remains unclear. Understanding these differences could lead to improvements in N use efficiency in ruminant diets by formulating more precise low-N diets considering the particularities for each species.

Physical parameters of bovine activated oocytes and zygotes as predictors of development success.

The worldwide production of in vitro-produced embryos in livestock species continues to grow. The current gold standard for selecting quality oocytes and embryos is morphologic assessment, yet this method is subjective and varies based on experience. There is a need for a non-invasive, objective method of selecting viable oocytes and embryos. The aim of this study was to determine if ooplasm area, diameter including zona pellucida (ZP), and ZP thickness of artificially activated oocytes and in vitro fertilized (IVF) zygotes are indicative of development success in vitro and correlated with embryo quality, as assessed by total blastomere number. Diameter affected the probability of development to the blastocyst stage in activated oocytes on day 7 (P < 0.01) and day 8 (P < 0.001), and had a tendency to affect IVF zygotes on day 8 (P = 0.08). Zona pellucida thickness affected the probability of development on day 7 (P < 0.01) and day 8 (P < 0.001) in activated oocytes, and day 8 for IVF zygotes (P < 0.05). An interaction between ZP thickness and diameter was observed on days 7 and 8 (P < 0.05) in IVF zygotes. Area did not significantly affect the probability of development, but was positively correlated with blastomere number on day 8 for IVF zygotes (P = 0.01, conditional R2 = 0.09). Physical parameters of bovine zygotes have the potential for use as a non-invasive, objective selection method. Upon further development, methods used in this study could be integrated into embryo production systems to improve IVF success.

Influence of two rumen cannulation techniques on postoperative recovery in sheep.

OBJECTIVE: To determine the influence of two methods of rumen cannulation on postoperative animal welfare. STUDY DESIGN: Experimental study. ANIMALS: Twelve commercial wethers (n = 6 per group). METHODS: Group 1 wethers underwent a one-step procedure to place a cannula immediately after fistulation of the rumen to the skin. Group 2 wethers underwent a two-step procedure in which a portion of the rumen was externalized and held with a clamp for 9 days, after which the cannula was placed into the fistula created by removal of the clamped rumen tissue. Feed intake and vital signs were monitored daily for 24 days postoperatively. Plasma fibrinogen and serum cortisol were measured daily for 14 days postoperatively to estimate inflammatory and stress responses, respectively. Change in body weight was also assessed. RESULTS: Cannulation method did not affect (P > .05) body weight, temperature, heart rate, respiratory rate, fibrinogen level, or cumulative cortisol level. Feed intake was lower (0.82 vs 1.2 kg/d; P < .0001), and mean cortisol level was greater (124.2 vs 121.5 ng/mL; P = .038) in group 2 compared with group 1. CONCLUSION: Although both cannulation methods mostly elicited similar physiological responses, animals seemed to experience more discomfort and stress when undergoing the two-step procedure. CLINICAL SIGNIFICANCE: Rumen cannulation performed in one step is recommended to improve postoperative welfare.

Nutritional and greenhouse gas impacts of removing animals from US agriculture.

As a major contributor to agricultural greenhouse gas (GHG) emissions, it has been suggested that reducing animal agriculture or consumption of animal-derived foods may reduce GHGs and enhance food security. Because the total removal of animals provides the extreme boundary to potential mitigation options and requires the fewest assumptions to model, the yearly nutritional and GHG impacts of eliminating animals from US agriculture were quantified. Animal-derived foods currently provide energy (24% of total), protein (48%), essential fatty acids (23-100%), and essential amino acids (34-67%) available for human consumption in the United States. The US livestock industry employs 1.6 × 106 people and accounts for $31.8 billion in exports. Livestock recycle more than 43.2 × 109 kg of human-inedible food and fiber processing byproducts, converting them into human-edible food, pet food, industrial products, and 4 × 109 kg of N fertilizer. Although modeled plants-only agriculture produced 23% more food, it met fewer of the US population's requirements for essential nutrients. When nutritional adequacy was evaluated by using least-cost diets produced from foods available, more nutrient deficiencies, a greater excess of energy, and a need to consume a greater amount of food solids were encountered in plants-only diets. In the simulated system with no animals, estimated agricultural GHG decreased (28%), but did not fully counterbalance the animal contribution of GHG (49% in this model). This assessment suggests that removing animals from US agriculture would reduce agricultural GHG emissions, but would also create a food supply incapable of supporting the US population's nutritional requirements.

Transcriptomic profiles of the bovine mammary gland during lactation and the dry period.

The initiation and maintenance of lactation are complex phenomena governed by biochemical and endocrine processes in the mammary gland (MG). Although DNA-based approaches have been used to study the onset of lactation, more comprehensive RNA-based techniques may be critical in furthering our understanding of gene alterations that occur to support lactation in the bovine MG. To further determine how gene profiles vary during lactation compared with the dry period, RNA-seq transcriptomic analysis was used to identify differentially expressed genes (DEG) in bovine MG tissues from animals that were lactating and not lactating. A total of 881 DEG (605 upregulated and 276 downregulated) were identified in MG of 3 lactating Chinese Holstein dairy cows versus the 3 dry cows. The subcellular analysis showed that the upregulated genes were most abundantly located in "integral to membrane" and "mitochondrion," and the top number of downregulated genes existed in "nucleus" and "cytoplasm." The functional analysis indicated that the DEG were primarily associated with the support of lactation processes. The genes in higher abundance were most related to "metabolic process," "oxidation-reduction process," "transport" and "signal transduction," protein synthesis-related processes (transcription, translation, protein modifications), and some MG growth-associated processes (cell proliferation/cycle/apoptosis). The downregulated genes were mainly involved in immune-related processes (inflammatory/immune/defense responses). The KEGG analysis suggested that protein synthesis-related pathways (such as protein digestion and absorption; protein processing in endoplasmic reticulum; and glycine, serine, and threonine metabolism) were highly and significantly enriched in the bovine MG of lactating cows compared to dry cows. The results suggested that the dry cows had decreased capacity for protein synthesis, energy generation, and cell growth but enhanced immune response. Collectively, this reduced capacity in dry cows supports the physiological demands of the next lactation and the coordinated metabolic changes that occur to support these demands. A total of 51 identified DEG were validated by RT-PCR, and consistent results were found between RT-PCR and the transcriptomic analysis. This work provides a profile of gene-associated changes that occur during lactation and can be used to facilitate further investigation of the mechanisms underlying lactation in dairy cows.

Plasma and Pancreas Islet Hormone Concentrations in Lactating Rats Are Associated with Dietary Protein Amounts.

Background: Circulating amino acid (AA) and nitric oxide (NO) concentrations and hepatic gluconeogenesis are affected by previous protein intake. However, information about their relations and islet hormone responses is limited. Objective: This study investigated the associations between islet hormone concentrations with circulating AA and NO concentrations as well as with hepatic gluconeogenesis in lactating rats. Methods: At delivery, 18 Wistar rats aged 14 wk were assigned either to low-protein (LP; 9% protein), standard-protein (SP; 21% protein), or high-protein (HP; 35% protein) diets for 15 d in groups of 6 pups/dam. Circulating AA and NO concentrations, circulating and pancreas islet hormone concentrations, and the activities and gene expressions of hepatic phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase) were measured at the end of treatment. Results: Circulating insulin and glucagon concentrations were greater in the HP than in the LP (25% and 17%, respectively) and SP (37% and 31%) diet groups, whereas compared with the SP group, pancreatic concentrations were lower in the LP (32% and 49%) and HP (34% and 46%) groups (P < 0.01). Hepatic PEPCK and G6Pase activities in the HP group were greater than those in the SP (15% and 15%) and LP (8% and 19%) groups (P < 0.05). In all groups, plasma NO concentrations were correlated negatively to circulating insulin (r = -0.77, P = 0.0003) and positively to pancreas insulin and glucagon concentrations and the insulin-to-glucagon ratio (r = 0.50-0.63; P < 0.05). Some circulating AAs correlated positively to circulating insulin and pancreas insulin and glucagon (r = 0.50-0.82, P < 0.05) but negatively to circulating glucagon (r = -0.53-0.68, P < 0.05). Conclusion: Variations in circulating AA and NO concentrations and hepatic gluconeogenic enzyme activities are likely intermediary responses involved in the effects of dietary protein amounts on the synthesis and secretion of islet hormones in lactating rats.

An evaluation of Molly cow model predictions of ruminal metabolism and nutrient digestion for dairy and beef diets.

Model evaluation, as a critical process of model advancement, is necessary to identify adequacy and consistency of model predictions. The objectives of this study were (1) to evaluate the accuracy of Molly cow model predictions of ruminal metabolism and nutrient digestion when simulating dairy and beef cattle diets; and (2) to identify deficiencies in representations of the biology that could be used to direct further model improvements. A total of 229 studies (n = 938 treatments) including dairy and beef cattle data, published from 1972 through 2016, were collected from the literature. Root mean squared errors (RMSE) and concordance correlation coefficients (CCC) were calculated to assess model accuracy and precision. Ruminal pH was very poorly represented in the model with a RMSE of 4.6% and a CCC of 0.0. Although volatile fatty acid concentrations had negligible mean (2.5% of mean squared error) and slope (6.8% of mean squared error) bias, the CCC was 0.28, implying that further modifications with respect to volatile fatty acid production and absorption are required to improve model precision. The RMSE was greater than 50% for ruminal ammonia and blood urea-N concentrations with high proportions of error as slope bias, indicating that mechanisms driving ruminal urea N recycling are not properly simulated in the model. Only slight mean and slope bias were exhibited for ruminal outflow of neutral detergent fiber, starch, lipid, total N, and nonammonia N, and for fecal output of protein, neutral detergent fiber, lipid, and starch, indicating the mechanisms encoded in the model relative to ruminal and total-tract nutrient digestion are properly represented. All variables related to ruminal metabolism and nutrient digestion were more precisely predicted for dairy cattle than for beef cattle. This difference in precision was mostly related to the model's inability to simulate low forage diets included in the beef studies. Overall, ruminal pH was poorly simulated and contributed to problems in ruminal nutrient degradation and volatile fatty acid production predictions. Residual analyses suggested ruminal ammonia concentrations need to be considered in the ruminal pH equation, and therefore the inaccuracies in predicting ruminal urea N recycling must also be addressed. These modifications to model structure will likely improve model performance across a wider array of dietary inputs and cattle type.

Technical note: Methodological and feed factors affecting prediction of ruminal degradability and intestinal digestibility of essential amino acids.

We hypothesized that ruminal degradability of essential AA (EAA) and the intestinal digestibility of the ruminally undegraded EAA residue in feeds could be evaluated in a meta-analysis. The objective was to characterize methodological factors for ruminal incubation (time of incubation of feed in situ) and method of simulating digestion of the ruminally undegraded AA (incubation of residue in digestive enzymes in vitro or in mobile bags inserted into the duodenum). To increase numbers of observations, feeds were categorized before ANOVA. An approach is described to predict differential ruminal degradability (or undegradability) of individual EAA by normalizing them as a proportion of total AA (TAA) degradability (undegradability) and similarly to normalize the intestinal digestibility of EAA using TAA. Interaction of feed category with individual EAA justifies future studies with a broader range of feeds and more replication within feed to bolster this approach. With broader data, the approach to normalize EAA as a proportion of TAA should allow a better defined EAA library to be integrated with more robust CP databases (that can be updated with specific feed information from more routine laboratory analyses) in dairy supply-requirement models.

Short communication: Evaluation of the PREP10 energy-, protein-, and amino acid-allowable milk equations in comparison with the National Research Council model.

The objective of this work was to evaluate the precision and accuracy of the milk yield predictions made by the PREP10 model in comparison to those from the National Research Council (NRC) Nutrient Requirements of Dairy Cattle. The PREP10 model is a ration-balancing system that allows protein use efficiency to vary with production level. The model also has advanced AA supply and requirement calculations that enable estimation of AA-allowable milk (MilkAA) based on 10 essential AA. A literature data set of 374 treatment means was collected and used to quantitatively evaluate the estimates of protein-allowable milk (MilkMP) and energy-allowable milk yields from the NRC and PREP10 models. The PREP10 MilkAA prediction was also evaluated, as were both models' estimates of milk based on the most-limiting nutrient or the mean of the estimated milk yields. For most milk estimates compared, the PREP10 model had reduced root mean squared prediction error (RMSPE), improved concordance correlation coefficient, and reduced mean and slope bias in comparison to the NRC model. In particular, utilizing the variable protein use efficiency for milk production notably improved the estimate of MilkMP when compared with NRC. The PREP10 MilkMP estimate had an RMSPE of 18.2% (NRC = 25.7%), concordance correlation coefficient of 0.82% (NRC = 0.64), slope bias of -0.14 kg/kg of predicted milk (NRC = -0.34 kg/kg), and mean bias of -0.63 kg (NRC = -2.85 kg). The PREP10 estimate of MilkAA had slightly elevated RMSPE and mean and slope bias when compared with MilkMP. The PREP10 estimate of MilkAA was not advantageous when compared with MilkMP, likely because AA use efficiency for milk was constant whereas MP use was variable. Future work evaluating variable AA use efficiencies for milk production is likely to improve accuracy and precision of models of allowable milk.

Physically adjusted neutral detergent fiber system for lactating dairy cow rations. I: Deriving equations that identify factors that influence effectiveness of fiber.

Physically effective neutral detergent fiber (peNDF) is the fraction of neutral detergent fiber (NDF) that stimulates chewing activity and contributes to the floating mat of large particles in the rumen. Multiplying dietary NDF by particle size has been used as an estimate of peNDF. In re-evaluating the concept of peNDF, we compared the use of peNDF as dietary NDF × particle size with the use of individual NDF and particle size descriptors (physically adjusted NDF; paNDF) when used with other physical and chemical diet descriptors to predict dry matter (DM) intake (DMI), rumination time, and ruminal pH in lactating dairy cows. The purpose is to ultimately use these equations to estimate diet adequacy to maintain ruminal conditions. Each response variable had 8 models in a 2 (peNDF, paNDF) × 2 (diet, diet and ruminal factors) × 2 (DM, as fed basis) factorial arrangement. Particle size descriptors were those determined with the Penn State Particle Separator. Treatment means (n = 241) from 60 publications were used in backward elimination multiple regression to derive models of response variables. When available, peNDF terms entered equations. Models containing peNDF terms had similar or lower unadjusted concordance correlation coefficients (an indicator of similar or lower accuracy and precision) than did models without peNDF terms. The peNDF models for rumen pH did not differ substantially from paNDF models. This suggests that peNDF can account for some variation in ruminal pH; however, overt advantages of peNDF were not apparent. Significant variables that entered the models included estimated mean particle size; as fed or DM proportions retained on 19- and 8-mm sieves of the Penn State Particle Separator; DMI; dietary concentrations of forage; forage NDF; CP; starch; NDF; rumen-degraded starch and rumen-degraded NDF; and the interaction terms of starch × mean particle size, acid detergent fiber/NDF, and rumination time/DMI. Many dietary factors beyond particle size and NDF were identified as influencing the response variables. In conclusion, these results appear to justify the development of a modeling approach to integrate individual physical and chemical factors to predict effects on factors affecting rumen conditions.

Physically adjusted neutral detergent fiber system for lactating dairy cow rations. II: Development of feeding recommendations.

The objective of this work was to leverage equations derived in a meta-analysis into an ensemble modeling system for estimating dietary physical and chemical characteristics required to maintain desired rumen conditions in lactating dairy cattle. Given the availability of data, responsiveness of ruminal pH to animal behaviors, and the chemical composition and physical form of the diet, mean ruminal pH was chosen as the primary rumen environment indicator. Physically effective fiber (peNDF) is defined as the fraction of neutral detergent fiber (NDF) that stimulates chewing activity and contributes to the floating mat of large particles in the rumen. The peNDF of feedstuffs is typically estimated by multiplying the NDF content by a particle size measure, resulting in an estimated index of effectiveness. We hypothesized that the utility of peNDF could be expanded and improved by dissociating NDF and particle size and considering other dietary factors, all integrated into a physically adjusted fiber system that can be used to estimate minimum particle sizes of TMR and diet compositions needed to maintain ruminal pH targets. Particle size measures of TMR were limited to those found with the Penn State particle separator (PSPS). Starting with specific diet characteristics, the system employed an ensemble of models that were integrated using a variable mixture of experts approach to generate more robust recommendations for the percentage of dietary DM material that should be retained on the 8-mm sieve of a PSPS. Additional continuous variables also integrated in the physically adjusted fiber system include the proportion of material (dry matter basis) retained on the 19- and 8-mm sieves of the PSPS, estimated mean particle size, the dietary concentrations of forage, forage NDF, starch, and NDF, and ruminally degraded starch and NDF. The system was able to predict that the minimum proportion of material (dry matter basis) retained on the 8-mm sieve should increase with decreasing forage NDF or dietary NDF. Additionally, the minimum proportion of dry matter material on the 8-mm sieve should increase with increasing dietary starch. Results of this study agreed with described interrelationships between the chemical and physical form of diets fed to dairy cows and quantified the links between NDF intake, diet particle size, and ruminal pH. Feeding recommendations can be interpolated from tables and figures included in this work.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction.

Several attempts have been made to quantify microbial protein flow from the rumen; however, few studies have evaluated tradeoffs between empirical equations (microbial N as a function of diet composition) and more mechanistic equations (microbial N as a function of ruminal carbohydrate digestibility). Although more mechanistic approaches have been touted because they represent more of the biology and thus might behave more appropriately in extreme scenarios, their precision is difficult to evaluate. The objective of this study was to derive equations describing starch, neutral detergent fiber (NDF), and organic matter total-tract and ruminal digestibilities; use these equations as inputs to equations predicting microbial N (MicN) production; and evaluate the implications of the different calculation methods in terms of their precision and accuracy. Models were evaluated based on root estimated variance σˆe and concordance correlation coefficients (CCC). Ruminal digestibility of NDF was positively associated with DMI and concentrations of NDF and CP and was negatively associated with concentration of starch and the ratio of acid detergent fiber to NDF (CCC=0.946). Apparent ruminal starch digestibility was increased by omasal sampling (compared with duodenal sampling), was positively associated with forage NDF and starch concentrations, and was negatively associated with wet forage DMI and total dietary DMI (CCC=0.908). Models were further evaluated by calculating fit statistics from a common data set, using stochastic simulation, and extreme scenario testing. In the stochastic simulation, variance in input variables were drawn from a multi-variate random normal distribution reflective of input measurement errors and predicting MicN while accounting for the measurement errors. Extreme scenario testing evaluated each MicN model against a data subset. When compared against an identical data set, predicting MicN empirically had the lowest prediction error, though differences were slight (σˆe 23.3% vs. 23.7 or 24.3%), and highest concordance (0.52 vs. 0.48 or 0.44) of any approach. Minimal differences were observed between empirical MicN prediction (σˆe 25.3%; CCC 0.530) and MicN prediction (σˆe 25.3%; CCC 0.532) from rumen carbohydrate digestibility in the stochastic analysis or extreme scenario testing. Despite the hypothesized benefits of a more mechanistic prediction approach, few differences between the calculation approaches were identified.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations.

The objective was to summarize the literature and derive equations that relate the chemical composition of diet and rumen characteristics to the intestinal supply of microbial nitrogen (MicN), efficiency of microbial protein synthesis (EMPS), and flow of nonammonia nonmicrobial N (NANMN). In this study, 619 treatment means from 183 trials were assembled for dairy cattle sampled from the duodenum or omasum. Backward elimination multiple regression was used to derive equations to estimate flow of nitrogenous components over a large range of dietary conditions. An intercept shift for sample location revealed that omasal sampling estimated greater MicN flow relative to duodenal sampling, but sample location did not interact with any other variables tested. The ruminal outflow of MicN was positively associated with dry matter intake (DMI) and with dietary starch percentage at a decreasing rate (quadratic response). Also, MicN was associated with DMI and rumen-degraded starch and neutral detergent fiber (NDF). When rumen measurements were included, ruminal pH and ammonia-N were negatively related to MicN flow along with a strong positive association with ruminal isovalerate molar proportion. When evaluating these variables with EMPS, isovalerate interacted with ammonia such that the slope for EMPS with increasing isovalerate increased as ammonia-N concentration decreased. A similar equation with isobutyrate confirms the importance of branched-chain volatile fatty acids to increase growth rate and therefore assimilation of ammonia-N into microbial protein. The ruminal outflow of NANMN could be predicted by dietary NDF and crude protein percentages, which also interacted. This result is probably associated with neutral detergent insoluble N contamination of NDF in certain rumen-undegradable protein sources. Because NANMN is calculated by subtracting MicN, sample location was inversely related compared with the MicN equation, and omasal sampling underestimated NANMN relative to duodenal sampling. As in the MicN equation, sampling location did not interact with any other variables tested for NANMN. Equations derived from dietary nutrient composition are robust across dietary conditions and could be used for prediction in protein supply-requirement models. These empirical equations were supported by more mechanistic equations based on the ruminal carbohydrate degradation and ruminal variables related to dietary rumen degradable protein.

Precision diet formulation to improve performance and profitability across various climates: Modeling the implications of increasing the formulation frequency of dairy cattle diets.

The objective of this study was to use a precision nutrition model to simulate the relationship between diet formulation frequency and dairy cattle performance across various climates. Agricultural Modeling and Training Systems (AMTS) CattlePro diet-balancing software (Cornell Research Foundation, Ithaca, NY) was used to compare 3 diet formulation frequencies (weekly, monthly, or seasonal) and 3 levels of climate variability (hot, cold, or variable). Predicted daily milk yield (MY), metabolizable energy (ME) balance, and dry matter intake (DMI) were recorded for each frequency-variability combination. Economic analysis was conducted to calculate the predicted revenue over feed and labor costs. Diet formulation frequency affected ME balance and MY but did not affect DMI. Climate variability affected ME balance and DMI but not MY. The interaction between climate variability and formulation frequency did not affect ME balance, MY, or DMI. Formulating diets more frequently increased MY, DMI, and ME balance. Economic analysis showed that formulating diets weekly rather than seasonally could improve returns over variable costs by $25,000 per year for a moderate-sized (300-cow) operation. To achieve this increase in returns, an entire feeding system margin of error of <1% was required. Formulating monthly, rather than seasonally, may be a more feasible alternative as this requires a margin of error of only 2.5% for the entire feeding system. Feeding systems with a low margin of error must be developed to better take advantage of the benefits of precision nutrition.

Rumen fermentation of meal-fed sheep in response to diets formulated to vary in fiber and protein degradability.

The concentration of volatile fatty acid (VFA) provides an imprecise view of VFA dynamics due to the confounding effects of fluid pool size and dynamics. Determination of VFA flux using isotope is expensive and a complex methodology. Therefore, a rapid and affordable approach to explore VFA dynamics may allow comprehensive characterization of VFA availability. The objective of this study was to explore the use of VFA dynamics generated by meal feeding to derive time-series rates of VFA apparent appearance and disappearance driven by different protein and fiber sources. Six ruminally cannulated wethers were fed diets containing timothy hay or beet pulp (TH and BP) and soybean meal (SBM) or heated soybean meal (HSBM). Diets were, TH + HSBM; TH + SBM; BP + HSBM; and BP + SBM and the experimental design was a partially replicated 4 × 4 Latin Square. Concentrations of VFA and polyethylene glycol (PEG) in rumen fluid samples were estimated. Concentrations of PEG were used to estimate fluid passage and volume to calculate VFA mass, and fluid-mediated exit. Maximum apparent appearance rate (mmol/h), the rate of apparent appearance decline (mmol/mmol/h), mean apparent appearance flux (mmol/h), mean apparent disappearance (mmol/h), and apparent disappearance rate (mmol/mmol/h) were estimated by deriving a 1 pool model for each VFA on a mass basis where appearance was assumed to follow an exponential decay pattern and disappearance followed mass-action kinetics. Statistical analyses were conducted using a linear mixed effect regression with fixed effects for fiber source, protein source, and their interaction, as well as random effects for animal and period. Rumen fluid volume (L) was greater in HSBM diets (P = 0.033) and fluid passage (%/h) was greater in SBM diets (P = 0.048). Concentrations (higher acetate and butyrate, P = 0.002 and 0.004, respectively) and molar proportions (higher valerate, P = 0.035) of VFA were affected only by fiber source; however, protein source and fiber source interacted to significantly influence apparent appearance rates and absorption rates of many major VFA. On a flux basis, HSBM supported significantly elevated mean disappearance of propionate (P = 0.033). This data demonstrates that time-series evaluation of fermentation dynamics, including fluid dynamics and VFA concentrations can be used to estimate apparent appearance and disappearance of VFA. Although further work is needed to confirm the alignment of these estimates with measurements of VFA supplies to the animal, this modeling approach may provide a simpler way to better understand the kinetics of rumen.

We estimated apparent appearance, apparent disappearance, appearance, and disappearance rates of rumen volatile fatty acid (VFA) of sheep in response to the different degradability of nutrients using time-series fermentation indicators with regular meal feeding. Two fiber sources (timothy hay [TH], and beet pulp [BP]) and two protein sources (soybean meal [SBM], and heat-treated soybean meal [HSBM]) were used in combination to prepare four dietary treatments. Polyethylene glycol (PEG) was used as the fluid marker to estimate rumen fluid volume and passage rate. The dynamics of VFA were estimated by deriving one pool model for individual VFA and concentrations of VFA, rumen fluid volume, and fluid passage rate were used in calculations. The interaction effect of protein and fiber source significantly influenced apparent appearance rates and disappearance rates of many major VFA. Significantly altered VFA dynamics, especially apparent disappearance was associated with HSBM-based diets. In conclusion, use of time-series evaluation of fermentation dynamics provides a minimal approach to integrate fluid dynamics and VFA concentrations to estimate apparent appearance and disappearance of VFA. With further development of this approach, we assume that estimated VFA dynamics will provide a better depiction of rumen VFA beyond concentrations and molar proportions in making inferences on rumen fermentation.

Effects of wearable therapies on jump performance in sport horses.

Introduction: Failure to properly prepare the equine athlete for exercise and support post-exercise recovery is a contributing factor to physical breakdown and lameness. Equine physiotherapy was not introduced until the early twentieth century and has since evolved to allow for wearable therapies such as therapeutic boots to be accessible to a broad spectrum of equestrians. The purpose of this study was to evaluate the effects of ceramic boots, boots combining vibration and cryotherapy, and boots containing tourmaline on the performance of sport horses during jumping as well as to examine changes in vital signs in response to treatment. Methods: Eight healthy horses received the 3 therapeutic boot treatments or a control (no boot) in a Latin square experiment for a period of 5 days each. Horses performed approximately 10 min of exercise through a jump chute for the 5 consecutive days and jump performance parameters were recorded during each exercise session. Therapeutics were applied in the morning prior to exercise per the manufacturer's recommendation and were removed only for exercise. Results and Discussion: In a Bayesian network analysis, changes in vital signs (heart rate, respiration, and temperature) were driven by individual animal, rather than boot treatment. Jump performance was influenced by boot treatment, physiological measurements, and individual animal. Therapeutic boots were associated with changes in conditional probabilities of numerous performance outcomes. This study indicates the use of wearable therapies may result in improved performance outcomes of sport horses in jumping exercises.

Network analysis to evaluate complexities in relationships among fermentation variables measured within continuous culture experiments.

The objective of this study was to leverage a frequentist (ELN) and Bayesian learning (BLN) network analyses to summarize quantitative associations among variables measured in 4 previously published dual-flow continuous culture fermentation experiments. Experiments were originally designed to evaluate effects of nitrate, defaunation, yeast, and/or physiological shifts associated with pH or solids passage rates on rumen conditions. Measurements from these experiments that were used as nodes within the networks included concentrations of individual volatile fatty acids, mM and nitrate, NO3-,%; outflows of non-ammonia nitrogen (NAN, g/d), bacterial N (BN, g/d), residual N (RN, g/d), and ammonia N (NH3-N, mg/dL); degradability of neutral detergent fiber (NDFd, %) and degradability of organic matter (OMd, %); dry matter intake (DMI, kg/d); urea in buffer (%); fluid passage rate (FF, L/d); total protozoa count (PZ, cells/mL); and methane production (CH4, mmol/d). A frequentist network (ELN) derived using a graphical LASSO (least absolute shrinkage and selection operator) technique with tuning parameters selected by Extended Bayesian Information Criteria (EBIC) and a BLN were constructed from these data. The illustrated associations in the ELN were unidirectional yet assisted in identifying prominent relationships within the rumen that were largely consistent with current understanding of fermentation mechanisms. Another advantage of the ELN approach was that it focused on understanding the role of individual nodes within the network. Such understanding may be critical in exploring candidates for biomarkers, indicator variables, model targets, or other measurement-focused explorations. As an example, acetate was highly central in the network suggesting it may be a strong candidate as a rumen biomarker. Alternatively, the major advantage of the BLN was its unique ability to imply causal directionality in relationships. Because the BLN identified directional, cascading relationships, this analytics approach was uniquely suited to exploring the edges within the network as a strategy to direct future work researching mechanisms of fermentation. For example, in the BLN acetate responded to treatment conditions such as the source of N used and the quantity of substrate provided, while acetate drove changes in the protozoal populations, non-NH3-N and residual N flows. In conclusion, the analyses exhibit complementary strengths in supporting inference on the connectedness and directionality of quantitative associations among fermentation variables that may be useful in driving future studies.

This study leveraged frequentist (ELN) and Bayesian networks (BLN) to evaluate the potential of network analysis to explore complex rumen environments with interconnected quantitative associations. The approaches were selected based on their capacity for holistic exploration of all possible quantitative associations among variables, including opportunities to explore the potential strength and directionality of those associations. Data from 4 continuous culture experiments, involving 18 rumen variables [major and minor volatile fatty acid (VFA), degradability variables and nitrogen related variables], were used for network derivation. Variables within a network are denoted as nodes and relationships between two nodes are referred to as edges. The different networking approaches had different strengths for biological interpretation. Although the ELN approach was useful for exploring the role and importance of specific variables in the network, the BLN had more relevance in selecting edges or relationships linking those variables. These strengths are complementary and make a case for joint exploration of datasets using both approaches. Many of the biological inferences derived from the networks are well-acknowledged within the literature, acetate, valerate, isobutyrate, and isovalerate were important nodes within both networks, and important edges focused on the driving role of N dynamics within the rumen. Overall, these analyses demonstrated potential to illustrate associations and directionality of quantitative associations among fermentation variables. These associations can be used to direct future studies based on more comprehensive datasets.

Finisher lamb growth and rumen fermentation responses to the plane of nutrition and naturally occurring coccidiosis.

The objective of the present study was to investigate the interaction of plane of nutrition and naturally occurring coccidiosis on finisher lamb growth performance, FAMACHA score, and rumen volatile fatty acid profile. The study included 30 Suffolk, Dorset or Suffolk x Dorset lambs and were divided into 2 groups based on their initial body weight and assigned to 2 feeding groups differing in dietary energy intake to create lambs representing divergent growth curves due to differing nutritional management. Lambs with naturally occurring coccidiosis and healthy lambs were present in both feeding groups making a 2 × 2 factorial arrangement of treatments, (a) high plane of nutrition (HPN) lambs with no clinical coccidiosis diagnosis (HPNH), (b) HPN lambs with clinical coccidiosis (HPNC), (c) low plane of nutrition (LPN) lambs with no clinical coccidiosis diagnosis (LPNH), (d) LPN lambs with clinical coccidiosis (LPNC). Body weight and FAMACHA scores were recorded once every 2 weeks. On d 65 of feeding, lambs were slaughtered, and rumen fluid samples were collected and analyzed for volatile fatty acid concentrations. All response variables were analyzed statistically using a linear mixed effects model with fixed effects for plane of nutrition, health status, and a random effect for initial body weight nested within the pen. The total and average weight gain were not associated with planes of nutrition, health status, or the interaction. Health status had an impact on FAMACHA© score (P = 0.047) and concentration of isobutyrate (P = 0.037) and tended to affect total VFA (P = 0.085) and acetate (P = 0.071) concentrations. The interaction between the plane of nutrition and the health status tended to affect butyrate concentration (P = 0.058). These data support the conclusion that coccidiosis infection impacted on rumen fermentation in a manner independent of the plane of nutrition; however, the translation of these rumen level impacts did not translate to the production responses.

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming.

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Precision technologies are revolutionizing animal agriculture by enhancing the management of animal welfare and productivity. To fully realize the potential benefits of precision livestock farming (PLF), the development and application of digital technologies are needed to facilitate the responsible and sustainable intensification of livestock production over the next several decades. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring audibility in value chains while assuaging concerns associated with labor shortages. In this paper, we analyze the multilayered network of sensors, actuators, communication, and analytics currently in use in PLF. We analyze the various aspects of sensing, communication, networking, and intelligence on the farm leveraging dairy farms as an example system. We also discuss the potential implications of advancements in communication, robotics, and artificial intelligence on the security and welfare of animals.