Ruminal pH sensing for monitoring volatile fatty acid concentrations in response to short-term dietary disruption.

The purpose of this study was to investigate the potential of using ruminal pH measurements to track time-series ruminal volatile fatty acid (VFA) concentrations occurring in response to short-term dietary disruption. Four ruminally cannulated dry Holstein dairy cows were individually housed and assigned to 4 treatments in a Latin square design. Treatments differing in forage-to-concentrate (F:C) ratio (100:0 to 55:45) were used because they were expected to result in large differences in VFA concentration, over which the relationships between pH and VFA could be robustly evaluated. Each sampling period lasted 36 h. Animals were removed from pasture and fasted for 24 h, after which time they were fed their treatment ration for 2 h and sampled for rumen fluid hourly for 12 h. Rumen fluid samples were analyzed immediately for pH, frozen, and subsequently analyzed for VFA concentrations using gas chromatography. Animals were returned to pasture for 7 d between sampling periods. To confirm that the short-term dietary disruptions resulted in expected variation in VFA concentrations, mean VFA concentrations during each animal period (n = 16) were analyzed using a linear mixed effects model with fixed (linear and quadratic) effects for F:C ratio and random effects for animal and period. Results indicated significant changes in VFA concentration across F:C ratio, but no significant shifts in VFA molar proportions, perhaps due to the short-term nature of the feeding protocol. To explore opportunity to use pH measurements to explain variability in VFA concentrations in real time across dietary conditions, a linear mixed-effect model was used to link the time-series measurements (n = 207). The VFA concentrations were analyzed with linear mixed effect models using linear and quadratic terms for pH, and random effects for animal and period. These models had poor accuracy, with residual error variance ranging from 21% to 38%, and residuals patterning significantly with F:C ratio. The data suggest that pH may lack reliability for VFA prediction in short-term feeding scenarios differing considerably in F:C ratio.

Ability of three dairy feed evaluation systems to predict postruminal outflows of amino acids in dairy cows: A meta-analysis.

Adequate prediction of postruminal outflows of essential AA (EAA) is the starting point of balancing rations for EAA in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System version 6.5.5 [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict EAA outflows (Trp was not tested). The data set included a total of 354 treatment means from 70 duodenal and 24 omasal studies. To avoid Type I error, mean and linear biases were considered of concern if statistically significant and representing > 5.0% of the observed mean. Analyses were conducted on raw observed values and on observations adjusted for the random effect of study. The analysis on raw data indicates the ability of the feed evaluation system to predict absolute values whereas the analysis on adjusted values indicates its ability to predict responses of EAA outflows to dietary changes. For the prediction of absolute values (based on raw data), NRC underpredicted outflows of all EAA, from 5.3 to 8.6% of the observed mean (%obs.mean) except for Leu, Lys, and Val; NASEM overpredicted Lys (10.8%obs.mean); and CNCPS overpredicted Arg, His, Lys, Met, and Val (5.2 to 26.0%obs.mean). No EAA had a linear bias of concern with NASEM, followed by NRC for His (6.8%obs.mean), and CNCPS for all EAA (5.6 to 12.2%obs.mean) except Leu, Phe, and Thr. On the other hand, for the prediction of responses to dietary changes (based on adjusted data), NRC had 2 EAA presenting a linear bias of concern, followed by NASEM and CNCPS with 4 and 6 EAA, respectively. Predictions of His showed a linear bias of concern (5.3 to 9.6%obs.mean) with the 3 feed evaluation systems. Measured chemistry of crude protein and EAA were reported for 1 or more feed ingredients of the ration in 36% of the studies, and resulted in decreased linear biases in the 3 feed evaluation systems. The difference in mean biases of Met outflows was systematically positive when comparing omasal versus duodenal studies. Predictions of Met outflows with NRC had a higher concordance correlation coefficient in duodenal (used to develop NRC equations) versus omasal studies, whereas the opposite was observed with CNCPS, the latter showing the lowest mean bias for Met in omasal sampling studies. The 30% difference in Met mean biases between sampling sites appeared related to a similar difference found for observed Met versus NAN outflows between duodenal and omasal studies, which is independent of predictions. In conclusion, NRC and NASEM yielded accurate predictions of EAA outflows, with a small superiority of NASEM to predict absolute values, and slight superiority of NRC to predict the responses to dietary changes. In comparison, CNCPS may present mean and linear biases of concern for many EAA. Moreover, it remains to determine which sampling site is more representative of the true supply of EAA to the cows.

Defining optimal dietary starch, oil, and amino acid inclusion levels for broilers experiencing a coccidiosis challenge.

Alternative methods to alleviate coccidiosis in broilers are of interest to producers, including dietary strategies to minimize disruptions in growth rate and efficiency when faced with health challenges. Our objective was to determine optimal combinations of dietary starch, amino acids (AA), and oil to benefit productivity of broilers experiencing Eimeria-induced immune activation. Two trials were conducted using 1,536 male Ross 308 broiler chicks in floor pens randomly assigned to 1 of 17 experimental treatments. All birds received common starter (d 0-10) and finisher (d 24-35) diets, and only differed based on their assigned experimental grower diet (d 10-24). Trial 1 experimental grower diets ranged from 2,700 to 3,300 kcal/kg AME. Trial 2 included 10 experimental grower diets following a simplex lattice design consisting of 3 basal lots formulated to have the highest starch (45.4%), oil (10.2%), or AA density (120, 1.33% digestible Lys) and mixed in 4 equally spaced levels for each component (0, 0.33, 0.67, 1). These mixtures enabled varying densities of AA (80-120% of recommendation), starch:oil (4:1-20:1), and AME (2,940-3,450 kcal/kg). Bird and feeder weights were collected on d 0, 10, 24, and 35, and birds were exposed to an Eimeria challenge on d 11 or 12. In trial 2, excreta samples were collected for AME determination and carcasses were processed on d 36. Data were analyzed using ANOVA, t test, or regression. In Trial 1, BW gain and feed conversion were improved (P < 0.05) by increasing dietary AME. In Trial 2, birds receiving diets containing AA at 93 to 107% of recommendations and higher oil exhibited improved (P < 0.05) performance, but increased starch at the expense of oil reduced performance (P < 0.05). Relative breast and fat pad weights were not influenced by diet in Trial 2. We determined that broilers mildly challenged with Eimeria would exhibit highest BW gain when receiving diets containing 35.8% starch, 8.9% oil, and 101.3% of AA recommendations, which can be utilized by producers to maintain productivity under health-challenged conditions.

Ability of three dairy feed evaluation systems to predict postruminal outflows of nitrogenous compounds in dairy cows: A meta-analysis.

Adequate prediction of postruminal outflow of protein fractions is the starting point for the determination of metabolizable protein supply in dairy cows. The objective of this meta-analysis was to compare the performance of 3 dairy feed evaluation systems (National Research Council [NRC], Cornell Net Protein and Carbohydrate System [CNCPS], and National Academies of Sciences, Engineering and Medicine [NASEM]) to predict outflows (g/d) of nonammonia nitrogren (NAN), microbial N (MiN), and nonammonia nonmicrobial N (NANMN). Predictions of rumen degradabilities (% of nutrient) of protein (RDP), NDF, and starch were also evaluated. The data set included 1,294 treatment means from 312 digesta flow studies. The 3 feed evaluation systems were compared using the concordance correlation coefficient (CCC), the ratio of root mean square prediction error (RMSPE) on standard deviation of observed values (RSR), and the slope between observed and predicted values. Mean and linear biases were deemed biologically relevant and are discussed if higher than a threshold of 5% of the mean of observed values. The comparisons were done on observed values adjusted or not for the study effect; the adjustment had a small effect on the mean bias but the linear bias reflected a response to a dietary change rather than absolute predictions. For the absolute predictions of NAN and MiN, CNCPS had the best-fit statistics (8% greater CCC; 6% lower RMSPE) without any bias; NRC and NASEM underpredicted NAN and MiN, and NASEM had an additional linear bias indicating that the underprediction of MiN increased at increased predictions. For NANMN, fit statistics were similar among the 3 feed evaluation systems with no mean bias; however, the linear bias with NRC and CNCPS indicated underprediction at low predictions and overprediction at elevated predictions. On average, the CCC were smaller and RSR ratios were greater for MiN versus NAN indicating increased prediction errors for MiN. For NAN responses to a dietary change, CNCPS also had the best predictions, although the mean bias with NASEM was not biologically relevant and the 3 feed evaluation systems did not present a linear bias. However, CNCPS, but not the 2 other feed evaluation systems, presented a linear bias for MiN, with responses being overpredicted at increased predictions. For NANMN, responses were overpredicted at increased predictions for the 3 feed evaluation systems, but to a lesser extent with NASEM. The site of sampling had an effect on the mean bias of MiN and NANMN in the 3 feed evaluation systems. The mean bias of MiN was higher in omasal than duodenal studies in the 3 feed evaluation systems (from 55 to 61 g/d) and this mean bias was twice as large when 15N labeling was used as a microbial marker compared with purines. Such a difference was not observed for duodenal studies. The reasons underlying these systematic differences are not clear as the type of measurements used in the current meta-analysis does not allow to delineate if one site or one microbial marker is yielding the "true" postruminal N outflows. Rumen degradabilities of protein was underpredicted with CNCPS, and RDP responses to a dietary change was underpredicted by the 3 feed evaluation systems with increased RDP predictions. Rumen degradability of NDF was underpredicted and had poor fit statistics for NASEM compared with CNCPS. Fit statistics were similar between CNCPS and NASEM for rumen degradability of starch, but with an underprediction of the response with NASEM and absolute values being overpredicted with CNCPS. Multivariate regression analyses showed that diet characteristics were correlated with prediction errors of N outflows in each feed evaluation system. Globally, compared with NAN and NANMN, residuals of MiN were correlated with several moderators in the 3 feed evaluation systems reflecting the complexity to measure and model this outflow. In addition, residuals of NANMN were correlated positively with RDP suggesting an overestimation of this parameter. In conclusion, although progress is still to be made to improve equations predicting postruminal N outflows, the current feed evaluation systems provide sufficient precision and accuracy to predict postruminal outflows of N fractions.

Global contributions of milk to nutrient supplies and greenhouse gas emissions.

Globally, ruminant production systems used to synthesize meat and milk differ tremendously in terms of their efficiency, productivity, and environmental impacts. Regardless of this variability, milk has been repeatedly identified as an essential source of nutrients for humans. The objective of this work was to characterize global contributions of fluid milk to human food and nutrient supplies, greenhouse gas emissions, and water withdrawal. Data were leveraged from the United Nations Food and Agriculture Organization to estimate global supplies of agricultural products. Trade of agricultural products and waste of those products, along with use of human food for livestock feed were accounted for before estimating human nutrient supplies. The contributions of milk to human-edible nutrient supplies were then enumerated in terms of their proportion of total nutrients supplied by the agricultural system and in terms of the human population's requirement for that nutrient. We identified that fluid milk provides over 10% of consumable supplies of vitamin B12, vitamin A, riboflavin, and calcium available for humans globally. In terms of human nutrient requirements, milk provides sufficient vitamin B12 to meet the needs of over 60% of the global population, riboflavin to satisfy 50% of the population, and calcium and phosphorus for over 35% of the population. Compared with other foods, milk ranked among the highest in terms of nutrient-to-calorie ratio for numerous amino acids, phosphorus, calcium, and riboflavin. Conditional dependencies were identified between greenhouse gas emissions and ruminant milk and meat, but not between water withdrawal and milk production. When evaluating the trade-offs in nutrient use versus nutrient provision for producing milk or producing all ruminant products, the production of ruminants worldwide was associated with both net increases and net decreases of several key nutrients. Continued work focusing on strategies to reduce the environmental impact of milk production will improve the utility of milk for feeding the growing global population.

The effects of dexamethasone administration on physiological, behavioral, and production parameters in dairy cows after a difficult calving.

Dairy cows are predisposed to diseases during the postpartum period. Dystocia has been associated with increased risk for disease, which is likely the result of increased tissue trauma and stress during the prolonged parturition. To attenuate the inflammatory response seen in dystocic animals and improve well-being, we assessed the effects of a glucocorticoid, dexamethasone administered within 12 h after calving. Dystocia was defined as a difficult birth resulting in a prolonged calving (≥70 min after the amniotic sac appears) and was monitored through 3 video cameras in the close-up dry-cow pen. Cows meeting the dystocia definition were randomly assigned to receive a single intramuscular injection of either dexamethasone (DEX; 0.1 mg/kg of body weight; n = 43) or saline (CON, n = 44) within 12 h following a dystocic calving. Serum haptoglobin, blood ß-hydroxybutyrate (BHB) concentrations, body temperature, and several behaviors were measured for the first 7 d postpartum. Additionally, milk production and components for the first 120 d were recorded. Using a mixed model, the fixed effects of treatment, parity, calving assistance, and time, along with 2- and 3-way interactions, were analyzed with cow as a random effect. We observed that primiparous DEX cows had greater serum haptoglobin concentrations on d 3 and d 7 postpartum compared with primiparous CON cows. There was no difference between treatment groups for blood BHB concentrations and body temperature. Behavior was altered between treatments, with DEX cows having reduced activity for the first week postpartum, as well as less restlessness and increased lying times on some of the days following calving. Treatment interacted with time for milk yield, such that DEX cows produced 2.7 kg/d less milk than CON cows for the first month following calving. The administration of dexamethasone resulted in changes in behavioral measurements, which could suggest a reduction in discomfort; however, due to the reduction in milk yield for the first month following calving, DEX administration may not be applicable for typical farm use. Additional research is needed to investigate treatments for cows experiencing dystocia without detrimental effects on milk yield.

Global human-edible nutrient supplies, their sources, and correlations with agricultural environmental impact.

Food production, sustainable development, population growth, and agricultural environmental impacts are linked global problems that require complex solutions. Many efforts evaluating these challenges primarily evaluate dietary strategies designed for health and environmental objectives without considering the subsequent adaptations required by the global food supply. Here we use a complementary approach to summarize trends and variability in the current agricultural system in the context of the growing population and impending environmental challenges. Globally, agricultural systems produce sufficient nutrients to feed 10 billion people with the exception of Ca, DHA + EPA, vitamins B4, D, and E. In a network analysis, greenhouse gas emissions were conditionally dependent on ruminant meat and milk, while water use was conditionally dependent on vegetable and fruit production; however, supplies of most nutrients were also dependent on these same production categories, suggesting trade-offs between nutritional and environmental objectives. Future work should evaluate strategies to address these compromises (i.e., improving water use efficiency and reducing greenhouse gas emissions), to explore to what extent such compromises are biophysically essential or merely a product of the current agricultural system structures. Given the time-sensitive nature of population growth and environmental concerns, strategies to make more effective use of currently produced agricultural products will also be critical complementary strategies to sustainably feed the growing population which can work in concert with other agricultural-, diet- and policy-focused efforts.

Comparison of production-related responses to hyperinsulinemia and hypoglycemia induced by clamp procedures or heat stress of lactating dairy cattle.

Hyperinsulinemia concurrent with hypoglycemia is one of a myriad of physiological changes typically experienced by lactating dairy cows exposed to heat stress, the consequences of which are not yet well defined or understood. Therefore, the objective of this experiment was to separate the production-related effects of hyperinsulinemia with hypoglycemia from those of a hyperthermic environment. Multiparous lactating Holstein cows (n = 23; 58 ± 4 d in milk, 3.1 ± 0.3 lactations) were housed in temperature-controlled rooms and all were subjected to 4 experimental periods as follows: (1) thermoneutral (TN; temperature-humidity index of 65.1 ± 0.2; d 1-5), (2) TN + hyperinsulinemic-hypoglycemic clamp (HHC; insulin infused at 0.3 µg/kg of BW per h, glucose infused to maintain 90 ± 10% of baseline blood glucose for 96 h; d 6-10), (3) heat stress (HS; temperature-humidity index of 72.5 ± 0.2; d 16-20), and (4) HS + euglycemic clamp (EC; glucose infused to reach 100 ± 10% of TN baseline blood glucose for 96 h; d 21-25). Cows were fed and milked twice daily. Feed refusals were collected once daily for calculation of daily dry matter intake, and milk samples were collected at the beginning and end of each period for component analyses. Circulating insulin concentrations were measured in daily blood samples, whereas glucose concentrations were measured more frequently and variably in association with clamp procedures. Rectal temperatures and respiration rates were greater during HS than TN, as expected, and states of hyperinsulinemia and hypoglycemia were successfully induced by the HHC and high ambient temperatures (HS and EC). Feed intake differed based upon thermal environment as it was similar during TN and HHC periods, and declined for HS and EC. Milk production was not entirely reflective of feed intake as it was greatest during TN, intermediate during HHC, and lowest during HS and EC. All milk components differed with the experimental period, primarily in response to the thermal environment. Interestingly, TN baseline glucose concentrations were highly correlated with the change in glucose from TN to HS, and were related to glycemic status during HS. Furthermore, although few in number, those cows that failed to become hypoglycemic during HS tended to have a greater reduction in milk yield. The work presented here addresses a critical knowledge gap by broadening our understanding of the physiological response to heat stress and the related changes in glycemic state. This broadened understanding is fundamental for the development of novel, innovative management strategies as the dairy industry is compelled to become increasingly efficient in spite of global warming.

Algorithm development for individualized precision feeding of supplemental top dresses to influence feed efficiency of dairy cattle.

Individualized, precision feeding of dairy cattle may contribute to profitable and sustainable dairy production. Feeding strategies targeted at optimizing efficiency of individual cows, rather than groups of animals with similar characteristics, is a logical goal of individualized precision feeding. However, algorithms designed to make feeding recommendations for specific animals are scarce. The objective of this study was to develop and test 2 algorithms designed to improve feed efficiency of individual cows by supplementing total mixed rations (TMR) with varying types and amounts of top-dressed feedstuffs. Twenty-four Holstein dairy cows were assigned to 1 of 3 treatment groups as follows: a control group fed a common TMR ad libitum, a group fed individually according to algorithm 1, and a group fed individually according to algorithm 2. Algorithm 1 used a mixed-model approach with feed efficiency as the response variable and automated measurements of production parameters and top-dress type as dependent variables. Cow was treated as a random effect, and cow by top-dress interactions were included if significant. Algorithm 2 grouped cows based on top-dress response efficiency structure using a principal components and k-means clustering. Both algorithms were trained over a 36-d experimental period immediately before testing, and were updated weekly during the 35-d testing period. Production performance responses for dry matter intake (DMI), milk yield, milk fat percentage and yield, milk protein percentage and yield, and feed efficiency were analyzed using a mixed-effects model with fixed effects for feeding algorithm, top dress, week, and the 2- and 3-way interactions among these variables. Milk protein percentage and feed efficiency were significantly affected by the 3-way interaction of top dress, algorithm, and week, and DMI tended to be affected by this 3-way interaction. Feeding algorithm did not affect milk yield, milk fat yield, or milk protein yield. However, feeding costs were reduced, and hence milk revenue increased on the algorithm-fed cows. The efficacy of feeding algorithms differed by top dress and time, and largely relied on DMI shifts to modulate feed efficiency. The net result, for the cumulative feeding groups, was that cows in the algorithm 1 and 2 groups earned over $0.45 and $0.70 more per head per day in comparison to cows on the TMR control, respectively. This study yielded 2 candidate approaches for efficiency-focused, individualized feeding recommendations. Refinement of algorithm selection, development, and training approaches are needed to maximize production parameters through individualized feeding.

Rumen fermentation and epithelial gene expression responses to diet ingredients designed to differ in ruminally degradable protein and fiber supplies.

Although numerous studies exist relating ruminal volatile fatty acid (VFA) concentrations to diet composition and animal performance, minimal information is available describing how VFA dynamics respond to diets within the context of the whole rumen environment. The objective of this study was to characterize how protein and fiber sources affect dry matter intake, rumen pH, fluid dynamics, fermentation parameters, and epithelial gene expression. Four diet treatments (soybean meal or heat-treated soybean meal and beet pulp or timothy hay) were delivered to 10 wethers. The soybean meals served as crude protein (CP) sources while the beet pulp and timothy hay represented neutral detergent fiber (NDF) sources. Feed intake, rumen pH, fluid pool size, and fluid passage rate were unaffected by treatment. Butyrate synthesis and absorption were greater on the beet pulp treatment whereas synthesis and absorption of other VFA remained unchanged. Both CP and NDF treatment effects were associated with numerous VFA interconversions. Expression levels of rumen epithelial genes were not altered by diet treatment. These results indicate that rumen VFA dynamics are altered by changes in dietary sources of nutrients but that intake, rumen environmental parameters, and the rumen epithelium may be less responsive to such changes.

Practical challenges and potential approaches to predicting low-incidence diseases on farm using individual cow data: A clinical mastitis example.

Clinical mastitis (CM) incidence is considerable in terms of cows affected per year, but cases are much less common in terms of detections per cow per milking. From a modeling perspective, where predictions are made every time any cow is milked, low CM incidence per cow day makes training, evaluating, and applying CM prediction models a challenge. The objective of this study was to build models for predicting CM incidence using time-series sensor data and choose models that maximize net return based on a cost matrix. Data collected from 2 university dairy farms, the University of Florida and Virginia Polytechnic Institute and State University, were used to gather representative data, including 110,156 milkings and 333 CM cases. Variables used in the models were milk yield, protein, lactose, fat, electrical conductivity, days in milk, lactation number, and activity as the number of steps, lying time, lying bouts, and lying bout duration. Models that predicted either likelihood of CM caused by gram-negative (GN) or gram-positive (GP) bacteria on each day were derived using extreme gradient boosting with weighting favoring true-positive cases, logistic responses, and log-loss errors. Model accuracies were determined using data randomly held out from the training set on each run. All variables considered were in terms of change (slope) over previous days, including the day CM was visually detected. The GN models had a median sensitivity (Se) of 52.6% and specificity (Sp) of 99.8%, whereas the GP models had a median Se of 37.5% and Sp of 99.9% when tested on the held-out data. In our models optimized to reduce cost from predictions, the Se was much less than Sp, suggesting that CM models might benefit from greater model weighting placed on Sp. Results also highlight the importance of positive predictive value (true positive cases per predicted positive case) along with Sp and Se, as models built on sparse data tend to predict too many false-positive cases. The calculated partial net return of our GN and GP models were -$0.15 and -$0.10 per cow per lactation, respectively, whereas International Organization for Standardization (ISO) standard models with Se of 80% and Sp of 99% would return -$1.32 per cow per lactation. Models chosen that minimized the cost to the farmer differed markedly from models that met ISO guidelines, showing asymmetry in targets between Sp and Se when the disease incidence rate is low. Because of the unique challenges that low-incidence diseases like CM present, we recommend that future CM predictive models consider the economic and practical implications in addition to the traditional model evaluation metrics.

A LoRa sensor network for monitoring pastured livestock location and activity.

Precision technologies for confinement animal agricultural systems have increased rapidly over the past decade, though precision technology solutions for pastured livestock remain limited. There are a number of reasons for this limited expansion of technologies for pastured animals, including networking availability and reliability, power requirements, and expense, among others. The objective of this work was to demonstrate a rapidly deployable long-range radio (LoRa) based, low-cost sensor suite that can be used to track location and activity of pastured livestock. The sensor is comprised of an inexpensive Arduino-compatible microprocessor, a generic MPU-9250 motion sensor which contains a 3-axis accelerometer, 3-axis magnetometer, and a 3-axis gyroscope, a generic GPS receiver, and a RFM95W generic LoRa radio. The microprocessor can be programmed flexibly using the open source Arduino IDE software to adjust the frequency of sampling, the data packet to send, and what conditions are needed to operate. The LoRa radio transmits to a Dragino LoRa gateway which can also be flexibly programmed through the Arduino IDE software to send data to local storage or, in cases where a web or cellular connection is available, to cloud storage. The sensor was powered using a USB cord connected to a 3,350 mAh lithium-ion battery pack. The Dragino gateway was programmed to upload data to the ThingSpeak IoT application programming interface for data storage, handling, and visualization. Evaluations showed minimal benefit associated with reducing sampling frequency as a strategy to preserve battery life. Packet loss ranged from 40% to 60%. In a 3 d evaluation on pastured sheep, the sensor suite was able to report GPS locations, inertial sensor readings, and temperature. Preliminary demonstrations of our system are satisfactory to detect animal location based on GPS data in real-time. This system has clear utility as a lower-cost strategy to deploy flexible, useful precision technologies for pasture-based livestock species.

Rumen volatile fatty acid molar proportions, rumen epithelial gene expression, and blood metabolite concentration responses to ruminally degradable starch and fiber supplies.

The objective of this work was to characterize rumen volatile fatty acid (VFA) concentrations, rumen epithelial gene expression, and blood metabolite responses to diets with different starch and fiber sources. Six ruminally cannulated yearling Holstein heifers (body weight = 330 ± 11.3 kg) were arranged in a partially replicated Latin square experiment with 4 treatments consisting of different starch [barley (BAR) or corn (CRN)] and fiber [timothy hay (TH) or beet pulp (BP)] sources. Treatments were arranged as a 2 × 2 factorial. Beet pulp and TH were used to create relative changes in apparent ruminal fiber disappearance, whereas CRN and BAR were used to create relative changes in apparent ruminal starch disappearance. Each period consisted of 3 d of diet adaptation and 15 d of dietary treatment. In situ disappearance of fiber and starch were estimated from bags incubated in the rumen from d 10 to 14. From d 15 to 17, rumen fluid was collected every hour from 0500 to 2300 h. Rumen fluid samples were pooled by animal/period and analyzed for pH and VFA concentrations. On d 18, 60 to 80 papillae were biopsied from the epithelium and preserved for gene expression analysis. On d 18, one blood sample per heifer was collected from the coccygeal vessel. In situ ruminal starch disappearance rate (7.30 to 8.72%/h for BAR vs. 7.61 to 10.5%/h for CRN) and the extent of fiber disappearance (22.2 to 33.4% of DM for TH vs. 34.4 to 38.7% of DM for BP) were affected by starch and fiber source, respectively. Analysis of VFA molar proportions showed a shift from propionate to acetate, and valerate to isovalerate on TH diets compared with BP. Corn diets favored propionate over butyrate in comparison to BAR diets. Corn diets also had higher molar proportions of valerate. Expression of 1 gene (SLC9A3) were increased in BP diets and 2 genes (BDH1 and SLC16A4) tended to be increased in TH diets. Plasma acetate demonstrated a tendency for a starch by fiber interaction with BAR-BP diets having the highest plasma acetate, but other metabolites measured were not significant. These results suggest that TH has the greatest effect on shifts in VFA molar proportions and epithelial transporters, but does not demonstrate shifts in blood metabolite concentrations.

Factors that affect heat production in lactating Jersey cows.

Heat production (HP) represents a major energy cost in lactating dairy cows. Better understanding of factors that affect HP will improve our understanding of energy metabolism. Our objective was to derive models to explain variation in HP of lactating Jersey cows. Individual animal-period data from 9 studies (n = 293) were used. The data set included cows with a wide range (min to max) in days in milk (44-410) and milk yield (7.8-43.0 kg/d). Diets included corn silage as the predominate forage source, but diets varied (min to max on DM basis) in crude protein (CP; 15.2-19.5%), neutral detergent fiber (NDF; 35.5-43.0%), starch (16.2-31.1%), and crude fat (2.2 to 6.4%) contents. Average HP was (mean ± standard deviation) 22.1 ± 2.86 Mcal/d, or 28.1 ± 3.70% of gross energy intake. Eight models were fit to explain variation in HP: (1) dry matter intake (DMI; INT); (2) milk fat, protein, and lactose yield (MILKCOMP); (3) INT and milk yield (INT+MY); (4) INT and MILKCOMP/DMI (INT+MILKCOMP); (5) mass of digested NDF, CP, and starch (DIG); (6) INT and digested energy (INT+DE); (7) INT and NDF, CP, and starch digestibility (INT+DIG); or (8) INT+MILKCOMP model plus urinary N excretion (INT+MILKCOMP+UN). For all HP models, metabolic body weight was included. All models were derived via a backward elimination approach and included the random effects of study, cow, and period within block within study. The INT models adequately explained variation in HP with a nonrandom effect-adjusted concordance correlation coefficient of 0.84. Similar adjusted concordance correlation coefficients (0.79-0.85) were observed for other HP models. The HP associated with milk protein yield and supply of digestible protein was greater than other milk production and nutrient digestibility variables. The HP associated with urinary N excretion was 5.32. Overall, HP can be adequately predicted from metabolic body weight and DMI. Milk component yield, nutrient digestibility, or urinary N excretion explained similar variation as DMI. Coefficients for milk protein and protein digestion suggest that digestion and metabolism of protein and synthesis of milk protein contribute substantially to HP of a dairy cow.

Contributions of dairy products to environmental impacts and nutritional supplies from United States agriculture.

Questions regarding the balance between the contribution to human nutrition and the environmental impact of livestock food products rarely evaluate specific species or how to accomplish the recommended depopulation. The objective of this study was to assess current contributions of the US dairy industry to the supply of nutrients and environmental impact, characterize potential impacts of alternative land use for land previously used for crops for dairy cattle, and evaluate the impacts of these approaches on US dairy herd depopulation. We modeled 3 scenarios to reflect different sets of assumptions for how and why to remove dairy cattle from the US food production system coupled with 4 land-use strategies for the potential newly available land previously cropped for dairy feed. Scenarios also differed in assumptions of how to repurpose land previously used to grow grain for dairy cows. The current system provides sufficient fluid milk to meet the annual energy, protein, and calcium requirements of 71.2, 169, and 254 million people, respectively. Vitamins supplied by dairy products also make up a high proportion of total domestic supplies from foods, with dairy providing 39% of the vitamin A, 54% of the vitamin D, 47% of the riboflavin, 57% of the vitamin B12, and 29% of the choline available for human consumption in the United States. Retiring (maintaining animals without milk harvesting) dairy cattle under their current management resulted in no change in absolute greenhouse gas emissions (GHGE) relative to the current production system. Both depopulation and retirement to pasture resulted in modest reductions (6.8-12.0%) in GHGE relative to the current agricultural system. Most dairy cow removal scenarios reduced availability of essential micronutrients such as α-linolenic acid, Ca, and vitamins A, D, B12, and choline. Those removal scenarios that did not reduce micronutrient availability also did not improve GHGE relative to the current production system. These results suggest that removal of dairy cattle to reduce GHGE without reducing the supply of the most limiting nutrients to the population would be difficult.

Breeding animals to feed people: The many roles of animal reproduction in ensuring global food security.

As the population grows and shifts demographically, the resulting increase in demand for beef and milk necessitates improvements in the sustainability of ruminant livestock production systems. Ruminant livestock contribute to ensuring global food security because they have the ability to up-cycle non-human-edible products into meat and milk products with notable nutritional value. However, ruminant livestock also pose a challenge to global food sustainability because they are resource-intensive to produce and contribute substantially to agricultural greenhouse gas emissions. As such, improving environmental impacts of ruminant livestock production globally is an essential goal. There are a number of strategies that can be employed to enhance sustainability of ruminant production systems; however, improving reproductive efficiency is among the more efficient, because an increase in reproductive success will reduce the number of cows needed to produce a target quantity of beef. This reduction in the cow herd size helps limit the number of unproductive animals retained in the herd, thereby reducing the environmental maintenance cost of livestock production. Additionally, proper application of reproductive technologies enables faster and more targeted advances in genetic gains, which can be leveraged to produce phenotypes that are resource-use-efficient and well-adapted to their production environment. Optimizing reproductive efficiency can be accomplished through improved genetic selection for fertility and fecundity; applying more effective use of assisted reproductive technologies; and coupling reproductive and nutritional management to optimize likelihood of reproductive success. Collectively, applying these approaches will be essential when working to ensure ruminant livestock's contribution to global food security.

Identifying gram-negative and gram-positive clinical mastitis using daily milk component and behavioral sensor data.

Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d -7 and -5, d -7 and -4, d -7 and -3, d -7 and -2, and d -7 and -1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d -10, -7, and -3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d -3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d -10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems.

Predictions of ruminal outflow of essential amino acids in dairy cattle.

The objective of this work was to update and evaluate predictions of essential AA (EAA) outflows from the rumen. The model was constructed based on previously derived equations for rumen-undegradable (RUP), microbial (MiCP), and endogenous (EndCP) protein outflows from the rumen, and revised estimates of ingredient composition and EAA composition of the protein fractions. Corrections were adopted to account for incomplete recovery of EAA during 24-h acid hydrolysis. The predicted ruminal protein and EAA outflows were evaluated against a data set of observed values from the literature. Initial evaluations indicated a minor mean bias for non-ammonia, non-microbial nitrogen flow ([RUP + EndCP]/6.25) of 16 g of N per day. Root mean squared errors (RMSE) of EAA predictions ranged from 26.8 to 40.6% of observed mean values. Concordance correlation coefficients (CCC) of EAA predictions ranged from 0.34 to 0.55. Except for Leu, all ruminal EAA outflows were overpredicted by 3.0 to 32 g/d. In addition, small but significant slope biases were present for Arg [2.2% mean squared error (MSE)] and Lys (3.2% MSE). The overpredictions may suggest that the mean recovery of AA from acid hydrolysis across laboratories was less than estimates encompassed in the recovery factors. To test this hypothesis, several regression approaches were undertaken to identify potential causes of the bias. These included regressions of (1) residual errors for predicted EAA flows on each of the 3 protein-driven EA flows, (2) observed EAA flows on each protein-driven EAA flow, including an intercept, (3) observed EAA flows on the protein-driven EAA flows, excluding an intercept term, and (4) observed EAA flows on RUP and MiCP. However, these equations were deemed unsatisfactory for bias adjustment, as they generated biologically unfeasible predictions for some entities. Future work should focus on identifying the cause of the observed prediction bias.

The effect of J5 bacterins on clinical, behavioral, and antibody response following an Escherichia coli intramammary challenge in dairy cows at peak lactation.

Vaccination against coliform mastitis has become part of mastitis control programs in the past 3 decades, as a means of reducing the severity of clinical mastitis. Our study objective was to evaluate the effect of 2 commercially available vaccines on clinical, behavioral, and antibody response following Escherichia coli intramammary challenge in cows near peak lactation. Cows (n = 12 per group) were vaccinated with vaccine 1 (V1) or vaccine 2 (V2) at dry-off, 21 d pre-calving, and 14 d post-calving. Twelve cows served as unvaccinated controls (CTL). Cows were challenged with E. coli in a rear quarter at approximately 100 d in milk. Milk samples were collected pre- and post-challenge to enumerate E. coli and determine somatic cell count. Serum was collected before each vaccination and at d 0, 1, 2, 3, 6, 30, and 60 relative to challenge, to study antibody response. Milk IgA and tumor necrosis factor-α concentrations were determined in whey. Vaginal temperature, cow activity, and milk yield and components were monitored post-challenge. Bacterial count, somatic cell score, milk yield and component decline, vaginal temperature, activity measures, and antibody and cytokine response were analyzed for treatment differences. The effects of parity, breed, and a repeated measure of time were also tested. Seven cows had to be removed from the study post-challenge for antibiotic treatment (CTL and V1, n = 3 each; V2, n = 1), 2 of which were euthanized (both CTL). Vaccinated cows exhibited fever (vaginal temperature ≥39.4°C) 3 h earlier than CTL cows, but we found no differences between treatments for bacterial count, somatic cell score, or milk yield reduction. Vaccinated cows spent more time lying per rest bout 2 d post-challenge, but total daily lying time was not different from CTL cows during the 7 d post-challenge. The vaccines differed in antibody response: V1 cows had greater serum IgG1 and IgG2 post-challenge. A parity effect was also evident: primiparous cows had lower bacterial counts, somatic cell score and a smaller milk yield decline than multiparous cows, but also had lower antibody production. Immunization with either J5 bacterin did not reduce clinical signs of mastitis in cows challenged at 100 d in milk, demonstrating that the effects of J5 vaccination had diminished at peak lactation.

Modeling portal-drained viscera and liver fluxes of essential amino acids in dairy cows.

The objective of this work was to predict essential amino acid (EAA) use and release by the portal-drained viscera (PDV) and liver of dairy cows. Previously derived equations were tested using data assembled from the literature, refit to the data, and modifications were undertaken to determine the best model for each EAA. The refitted model has the same structure as the original equations but is parameterized using a database of group means, as the original equations were derived using a single study with individual cow data and found to be biased. The PDV clearance model predicted portal vein concentrations given inputs of absorbed and arterial fluxes of EAA with root mean squared errors (RMSE) ranging from 3.3 to 12.1% of the observed means, and concordance correlation coefficients (CCC) ranging from 0.86 to 0.99 when using previously reported parameters. The reparameterized model generated from the assembled data set resulted in predictions of EAA portal vein concentrations with RMSE ranging from 3.2 to 8.6% and CCC ranging from 0.93 to 1.00. Slope bias ranged from 12.4 to 55.3% of mean squared errors and was correlated with arterial EAA concentrations. Modifying the model to allow rate constants to vary as a function of arterial EAA concentrations reduced slope bias, resulting in RMSE ranging from 1.9 to 6.5% and CCC from 0.97 to 1.00. Alternatively, splitting the model to account for use of EAA from absorption separately from arterial use resulted in poorer predictions and biologically infeasible parameter estimates. The liver clearance model predicted hepatic vein concentrations from arterial and portal vein input fluxes with RMSE across EAA ranging from 1.9 to 6.8% and CCC ranging from 0.97 to 1.00 when using reported parameters. The reparameterized model generated from the assembled data set resulted in predictions of EAA hepatic vein concentrations with RMSE ranging from 1.9 to 6.7% and CCC ranging from 0.97 to 1.00. Significant slope bias was present for Arg, His, Lys, Phe, Thr, and Val. Altering the model to represent the clearance rate constant as a function of arterial concentrations resulted in RMSE ranging from 1.8 to 6.5% and CCC ranging from 0.97 to 1.00. The combination of PDV and liver clearance models provided predictions of total splanchnic use similar to those of an empirical model representing splanchnic use as a fractional proportion of absorption that had RMSE ranging from 3.0 to 8.6% and CCC ranging from 0.95 to 0.99, with significant slope bias for the majority of EAA.