Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders.

In the United States, dairy calves are typically housed individually due to the perception of reduced risk of spreading infectious diseases between calves and the ability to monitor health on an individual calf basis. However, automated milk feeders (AMF) can provide individual monitoring of group-housed calves while allowing them to express more natural feeding behaviors and interact with each other. Research has shown that feeding behaviors recorded by AMF can be a helpful screening tool for detecting disease in dairy calves. Altogether, there is an opportunity to use the data from AMF to create a more robust and efficient model to predict disease, reducing the need for visual observation. Therefore, the objective of this observational study was to predict disease in preweaning dairy calves using AMF feeding behavior data and machine learning (ML) algorithms. This study was conducted on a dairy farm located in the Upper Midwest United States and visited weekly from July 2018 to May 2019. During farm visits, AMF data and calves' treatment records were collected, and calves were visually health-scored for attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, and cough score. The final datasets used for the analyses consisted of 740 and 741 calves, with 1,007 (healthy = 594 and sick = 413) and 1,044 (healthy = 560 and sick = 484) observations (health events) for Data 1 and Data 2, respectively. Data 1 included only the weekly calf health scores observed by research personnel, whereas Data 2 included primarily daily calf treatment records by farm staff in addition to weekly health scores. Calf visit-level feeding behaviors from AMF data included milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per d), and the interval between visits (min). Three approaches were used to predict health status: generalized linear model, random forest, and gradient boosting machine. A total of 16 models were built using different combinations of behavior parameters, including the number of rewarded visits, number of unrewarded visits, visit duration, the interval between visits, intake, intake divided by rewarded visits, drinking speed, and drinking speed divided by rewarded visits, and also calf age at the sick day as predictor variables. Of all algorithms, random forest and gradient boosting had the best performance predicting the health status of dairy calves. The results indicated that weekly health scores were not enough to predict calf health status. However, daily treatment records and AMF data were sufficient for creating predictive algorithms (e.g., F1-scores of 0.775 and 0.784 for random forest and gradient boosting, Data 2). This study suggests that ML was effective in determining the specific visit-level feeding behaviors that can be used to predict disease in group-housed preweaning dairy calves. Implementing these ML algorithms could reduce the need for visual calf observation on farms, minimizing labor time and improving calf health.

Increasing the prepartum dose of rumen-protected choline: Effects on milk production and metabolism in high-producing Holstein dairy cows.

Peripartum rumen-protected choline (RPC) supplementation is beneficial for cow health and production, yet the optimal dose is unknown. In vivo and in vitro supplementation of choline modulates hepatic lipid, glucose, and methyl donor metabolism. The objective of this experiment was to determine the effects of increasing the dose of prepartum RPC supplementation on milk production and blood biomarkers. Pregnant multiparous Holstein cows (n = 116) were randomly assigned to one of 4 prepartum choline treatments that were fed from -21 d relative to calving (DRTC) until calving. From calving until +21 DRTC, cows were fed diets targeting 0 g/d choline ion (control, CTL) or the recommended dose (15 g/d choline ion; RD) of the same RPC product that they were fed prepartum. The resulting treatments targeted: (1) 0 g/d pre- and postpartum [0.0 ± 0.000 choline ion, percent of dry matter (%DM); CTL]; (2) 15 g/d pre- and postpartum of choline ion from an established product (prepartum: 0.10 ± 0.004 choline ion, %DM; postpartum: 0.05 ± 0.004 choline ion, %DM; ReaShure, Balchem Corp.; RPC1RD▸RD); (3) 15 g/d pre- and postpartum of choline ion from a concentrated RPC prototype (prepartum: 0.09 ± 0.004 choline ion, %DM; postpartum: 0.05 ± 0.003 choline ion, %DM; RPC2, Balchem Corp.; RPC2RD▸RD); or (4) 22 g/d prepartum and 15 g/d postpartum from RPC2 [prepartum: 0.13 ± 0.005 choline ion, %DM; postpartum: 0.05 ± 0.003 choline ion, %DM; high prepartum dose (HD), RPC2HD▸RD]. Treatments were mixed into a total mixed ration, and cows had ad libitum access via a roughage intake control system (Hokofarm Group). From calving to +21 DRTC, all cows were fed a common base diet and treatments were mixed into the total mixed ration (supplementation period, SP). Thereafter, all cows were fed a common diet (0 g/d choline ion) until +100 DRTC (postsupplementation period, postSP). Milk yield was recorded daily and composition analyzed weekly. Blood samples were obtained via tail vessel upon enrollment, approximately every other day from -7 to +21 DRTC, and at +56 and +100 DRTC. Feeding any RPC treatment reduced prepartum dry matter intake compared with CTL. During the SP, no evidence for a treatment effect on energy-corrected milk (ECM) yield was found, but during the postSP, RPC1RD▸RD and RPC2RD▸RD treatments tended to increase ECM, protein, and fat yields. During the postSP, the RPC1RD▸RD and RPC2RD▸RD treatments tended to increase, and RPC2HD▸RD increased, the de novo proportion of total milk fatty acids. During the early lactation SP, RPC2HD▸RD tended to increase plasma fatty acids and ß-hydroxybutyrate concentrations, and RPC1RD▸RD and RPC2RD▸RD reduced blood urea nitrogen concentrations compared with CTL. The RPC2HD▸RD treatment reduced early lactation serum lipopolysaccharide binding protein compared with CTL. Overall, peripartum RPC supplementation at the recommended dose tended to increase ECM yield postSP, but no evidence was seen of an additional benefit on milk production with an increased prepartum dose of choline ion. The effects of RPC on metabolic and inflammatory biomarkers support the potential for RPC supplementation to affect transition cow metabolism and health and may support the production gains observed.

Assessing optimal frequency for image acquisition in computer vision systems developed to monitor feeding behavior of group-housed Holstein heifers.

Computer vision systems have emerged as a potential tool to monitor the behavior of livestock animals. Such high-throughput systems can generate massive redundant data sets for training and inference, which can lead to higher computational and economic costs. The objectives of this study were (1) to develop a computer vision system to individually monitor detailed feeding behaviors of group-housed dairy heifers, and (2) to determine the optimal frequency of image acquisition to perform inference with minimal effect on feeding behavior prediction quality. Eight Holstein heifers (96 ± 6 d old) were housed in a group and a total of 25,214 images (1 image every second) were acquired using 1 RGB camera. A total of 2,209 images were selected and each animal in the image was labeled with its respective identification (1-8). The label was annotated only on animals that were at the feed bunk (head through the feed rail). From the labeled images, 1,392 were randomly selected to train a deep learning algorithm for object detection with YOLOv3 ("You Only Look Once" version 3) and 154 images were used for validation. An independent data set (testing set = 663 out of the 2,209 images) was used to test the algorithm. The average accuracy for identifying individual animals in the testing set was 96.0%, and for each individual heifer from 1 to 8 the accuracy was 99.2, 99.6, 99.2, 99.6, 99.6, 99.2, 99.4, and 99.6%, respectively. After identifying the animals at the feed bunk, we computed the following feeding behavior parameters: number of visits (NV), mean visit duration (MVD), mean interval between visits (MIBV), and feeding time (FT) for each heifer using a data set composed by 8,883 sequential images (1 image every second) from 4 time points. The coefficient of determination (R2) was 0.39, 0.78, 0.48, and 0.99, and the root mean square error (RMSE) were 12.3 (count), 0.78, 0.63, and 0.31 min for NV, MVD, MIBV, and FT, respectively, considering 1 image every second. When we moved from 1 image per second to 1 image every 5 (MIBV) or 10 (NV, MDV, and FT) s, the R2 observed were 0.55 (NV), 0.74 (MVD), 0.70 (MIBV), and 0.99 (FT); and the RMSE were 2.27 (NV, count), 0.38 min (MVD), 0.22 min (MIBV), and 0.44 min (FT). Our results indicate that computer vision systems can be used to individually identify group-housed Holstein heifers (overall accuracy = 99.4%). Based on individual identification, feeding behavior such as MVD, MIBV, and FT can be monitored with reasonable accuracy and precision. Regardless of the frequency for optimal image acquisition, our results suggested that longer time intervals of image acquisition would reduce data collecting and model inference while maintaining adequate predictive performance. However, we did not find an optimal time interval for all feeding behavior; instead, the optimal frequency of image acquisition is phenotype-specific. Overall, the best R2 and RMSE for NV, MDV, and FT were achieved using 1 image every 10 s, and for MIBV it was achieved using 1 image every 5 s, and in both cases model inference and data storage could be drastically reduced.

Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States.

Automated milk feeders (AMF) are an attractive option for producers interested in adopting practices that offer greater behavioral freedom for calves and can potentially improve labor management. These feeders give farmers the opportunity to have a more flexible labor schedule and more efficiently feed group-housed calves. However, housing calves in group systems can pose challenges for monitoring calf health on an individual basis, potentially leading to increased morbidity and mortality. Feeding behavior recorded by AMF software could potentially be used as an indicator of disease. Therefore, the objective of this observational study was to investigate the association between feeding behaviors and disease in preweaning group-housed dairy calves fed with AMF. The study was conducted at a dairy farm located in the Upper Midwest United States and included a final data set of 599 Holstein heifer calves. The farm was visited on a weekly basis from May 2018, to May 2019, when calves were visually health scored and AMF data were collected. Calf health scores included calf attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, cough score, and rectal temperatures. Generalized additive mixed models (GAMM) were used to identify associations between feeding behavior and disease. The final quasibinomial GAMM included the fixed (main and interactions) effects of feeding behavior at calf visit-level including milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per day), and interval between visits (min), as well as the random effects of calf age in regard to their relationship with calf health status. Total milk intake (mL/d), drinking speed (mL/min), interval between visits (min) to the AMF, calf age (d), and rewarded visits were significantly associated with dairy calf health status. These results indicate that as total milk intake and drinking speed increased, the risk of calves being sick decreased. In contrast, as the interval between visits and age increased, the risk of calves being sick also increased. This study suggests that AMF data may be a useful screening tool for detecting disease in dairy calves. In addition, GAMM were shown to be a simple and flexible approach to modeling calf health status, as they can cope with non-normal data distribution of the response variable, capture nonlinear relationships between explanatory and response variables and accommodate random effects.

Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering.

Our aim was to explore whether changes in plasma essential AA (EAA) concentration ([EAA]p) or profile (defined here as the molar proportion of individual [EAA]p relative to the total [EAA]p) may serve as an indicator of the EAA status of a cow. We undertook a meta-analysis with the objectives to determine if different plasma EAA profiles exist among cows and to explore the association of [EAA]p or the profile of EAA with lactating cow performance and measures of N utilization. We hypothesized the existence of differences in [EAA]p and different plasma EAA profile for cows with greater milk output, feed efficiency, and greater N use efficiency (NUE; milk true protein-N:N intake) compared with cows with lower milk output, feed efficiency, and lower NUE. The data set included 22 feeding trials and 96 dietary treatments. First, a mixed-effect model analysis was used to predict [EAA]p in response to the categorical fixed effect of EAA, continuous fixed effect of National Research Council model-predicted metabolizable protein (MP) supply, continuous fixed effect of body weight, the fixed effect of EAA and MP supply interaction, the fixed effect of EAA and body weight interaction, and the random effect of study. Then, residuals of the model were standardized based on Z-score and clustered using the hierarchical method (Euclidean distance and Ward's minimum variance method) resulting in 2 clusters. Finally, a fixed-effect model was used to evaluate the significance with which clusters were associated with [EAA]p, cow performance, feed efficiency, and NUE. The total concentration of [EAA]p was lower (784 vs. 983 µM) and the concentration of each EAA was on average 22 µM lower for cows in cluster 1 compared with cluster 2 with the smallest and greatest difference found for Met (4 µM) and Val (59 µM), respectively. The percentage difference in [EAA]p was the smallest for Thr (-5.3%) and the greatest for Leu (-37.1%). There was no difference between clusters for Arg, His, and Met molar proportions; however, cows in cluster 1 had a lower molar proportion of Leu and a tendency for lower molar proportion of Val compared with cows in cluster 2. Additionally, cows in cluster 1 had greater molar proportions of Ile, Lys, and Thr and a tendency for greater molar proportion of Phe compared with cows in cluster 2. The fixed-effect model analysis indicated that cows in cluster 1 had higher milk energy output (+3.2 Mcal/d), true protein yield (+87 g/d) and fat yield (+236 g/d), feed efficiency (milk Mcal:dry matter intake; +8% unit), and a tendency for greater MP efficiency (Milk true protein/MP supply; +2.3% unit) than cows in cluster 2. These results suggested greater use of EAA by the mammary gland (as reflected by greater milk protein synthesis) and lower hepatic catabolism of AA (as reflected by a tendency to greater MP efficiency) in cows of cluster 1 compared with cluster 2. Our findings should be evaluated further, including whether the relative molar proportions of plasma EAA might serve as a holistic indicator of the EAA status of cows as related to their productivity, feed efficiency and N utilization.

Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves.

Bovine anaplasmosis causes considerable economic losses in dairy cattle production systems worldwide, ranging from $300 million to $900 million annually. It is commonly detected through rectal temperature, blood smear microscopy, and packed cell volume (PCV). Such methodologies are laborious, costly, and difficult to systematically implement in large-scale operations. The objectives of this study were to evaluate (1) rumination and activity data collected by Hr-Tag sensors (SCR Engineers Ltd.) in heifer calves exposed to anaplasmosis; and (2) the predictive ability of recurrent neural networks in early identification of anaplasmosis. Additionally, we aimed to investigate the effect of time series length before disease diagnosis (5, 7, 10, or 12 consecutive days) on the predictive performance of recurrent neural networks, and how early anaplasmosis disease can be detected in dairy calves (5, 3, and 1 d in advance). Twenty-three heifer calves aged 119 ± 15 (mean ± SD) d and weighing 148 ± 20 kg of body weight were challenged with 2 × 107 erythrocytes infected with UFMG1 strain (GenBank no. EU676176) isolated from Anaplasma marginale. After inoculation, animals were monitored daily by assessing PCV. The lowest PCV value (14 ± 1.8%) and the finding of rickettsia on blood smears were used as a criterion to classify an animal as sick (d 0). Rumination and activity data were collected continuously and automatically at 2-h intervals, using SCR Heatime Hr-Tag collars. Two time series were built including last sequence of -5, -7, -10, or -12 d preceding d 0 or a sequence of 5, 7, 10, or 12 d randomly selected in a window from -50 to -15 d before d 0 to ensure a sequence of days in which PCV was considered normal (32 ± 2.4%). Long short-term memory was used as a predictive approach, and a leave-one-animal-out cross-validation (LOAOCV) was used to assess prediction quality. Anaplasmosis disease reduced 34 and 11% of rumination and activity, respectively. The accuracy, sensitivity, and specificity of long short-term memory in detecting anaplasmosis ranged from 87 to 98%, 83 to 100%, and 83 to 100%, respectively, using rumination data. For activity data, the accuracy, sensitivity, and specificity varied from 70 to 98%, 61 to 100%, and 74 to 100%, respectively. Predictive performance did not improve when combining rumination and activity. The use of longer time-series did not improve the performance of models to predict anaplasmosis. The accuracy and sensitivity in predicting anaplasmosis up to 3 d before clinical diagnosis (d 0) were greater than 80%, confirming the possibility for early identification of anaplasmosis disease. These findings indicate the great potential of wearable sensors in early identification of anaplasmosis diseases. This could positively affect the profitability of dairy farmers and animal welfare.

Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables.

Predicting dry matter intake (DMI) and feed efficiency by leveraging the use of data streams available on farm could aid efforts to improve the feed efficiency of dairy cattle. Residual feed intake (RFI) is the difference between predicted and observed feed intake after accounting for body size, body weight change, and milk production, making it a valuable metric for feed efficiency research. Our objective was to develop and evaluate DMI and RFI prediction models using multiple linear regression (MLR), partial least squares regression, artificial neural networks, and stacked ensembles using different combinations of cow descriptive, performance, sensor-derived behavioral (SMARTBOW; Zoetis), and blood metabolite data. Data were collected from mid-lactation Holstein cows (n = 124; 102 multiparous, 22 primiparous) split equally between 2 replicates of 45-d duration with ad libitum access to feed. Within each predictive approach, 4 data streams were added in sequence: dataset M (week of lactation, parity, milk yield, and milk components), dataset MB (dataset M plus body condition score and metabolic body weight), dataset MBS (dataset MB plus sensor-derived behavioral variables), and dataset MBSP (dataset MBS plus physiological blood metabolites). The combination of 4 datasets and 4 analytical approaches resulted in 16 analyses of DMI and RFI, using variables averaged within cow across the study period. Additional models using weekly averaged data within cow and study were built using all predictive approaches for datasets M, MB, and MBS. Model performance was assessed using the coefficient of determination, concordance correlation coefficient, and root mean square error of prediction. Predictive models of DMI performed similarly across all approaches, and models using dataset MBS had the greatest model performance. The best approach-dataset combination was MLR-dataset MBS, although several models performed similarly. Weekly DMI models had the greatest performance with MLR and partial least squares regression approaches. Dataset MBS models had incrementally better performance than datasets MB and M. Within each approach-dataset combination, models with DMI averaged over the study period had slightly greater model performance than DMI averaged weekly. Predictive performance of all RFI models was poor, but slight improvements when using MLR applied to dataset MBS suggest that rumination and activity behaviors may explain some of the variation in RFI. Overall, similar performance of MLR, compared with machine learning techniques, indicates MLR may be sufficient to predict DMI. The improvement in model performance with each additional data stream supports the idea of integrating data streams to improve model predictions and farm management decisions.

Technical note: Comparison of dry matter measurements from handheld near-infrared units with oven drying at 60°C for 48 hours and other on-farm methods.

This study compared dry matter (DM) predictions of 3 handheld near-infrared spectrophotometer (NIRS) units (Moisture Tracker, Digi-Star Inc., Fort Atkinson, WI) to conventional oven drying at 60°C using 2 alfalfa and 2 corn silages. In addition, on-farm DM methods [microwave, Koster tester (Koster Moisture Tester Inc., Brunswick, OH), and food dehydrator methods] were also compared. Corn and alfalfa silages (1,600 g) obtained from the University of Wisconsin Dairy Cattle Center (DCC) and the Arlington Research Station (ARS) were analyzed for DM daily for 20 d. Two NIRS calibration methods were also tested within each unit. The DM predicted from the factory-preset calibrations was NIRf. The adjusted DM prediction was NIRa, where the average difference between oven-dried and NIRf determined on duplicate forage samples for 3 d before the experiment was used as a bias adjustment for all subsequent DM determinations. The average predicted DM from the 20 scans was recorded as the forage DM. The process was repeated 3 times with each NIRS unit. Two 100-g subsamples of each forage were also oven-dried for 48 h at 60°C daily in a forced-air oven. Oven DM of ARS and DCC alfalfa silages were 37.3 ± 1.1% and 48.5 ± 1.9%, respectively (mean ± standard deviation). Oven DM of ARS and DCC corn silages were 34.7 ± 1.2% and 37.4 ± 0.5%, respectively (mean ± standard deviation). Dry matter determinations from NIRf were on average 3.5 units higher than the oven DM values. The NIRa DM predictions were on average 1.7 DM units lower than the oven DM values. Additionally, differences among the 3 NIRf probe results were detected (43.1, 40.7, and 41.3% DM, respectively), but all other results were similar between probes. Determinations of DM by the microwave and food dehydrator were also similar with the 60°C, 48-h oven method, whereas the Koster tester was lower than the oven. The handheld NIRS units more accurately predicted DM content of the alfalfa silage but were not as accurate with corn silages when the factory preset calibrations were corrected for bias.

Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows.

Feed intake is one of the most important components of feed efficiency in dairy systems. However, it is a difficult trait to measure in commercial operations for individual cows. Milk spectrum from mid-infrared spectroscopy has been previously used to predict milk traits, and could be an alternative to predict dry matter intake (DMI). The objectives of this study were (1) to evaluate if milk spectra can improve DMI predictions based only on cow variables; (2) to compare artificial neural network (ANN) and partial least squares (PLS) predictions; and (3) to evaluate if wavelength (WL) selection through Bayesian network (BN) improves prediction quality. Milk samples (n = 1,279) from 308 mid-lactation dairy cows [127 ± 27 d in milk (DIM)] were collected between 2014 and 2016. For each milk spectra time point, DMI (kg/d), body weight (BW, kg), milk yield (MY, kg/d), fat (%), protein (%), lactose (%), and actual DIM were recorded. The DMI was predicted with ANN and PLS using different combinations of explanatory variables. Such combinations, called covariate sets, were as follows: set 1 (MY, BW0.75, DIM, and 361 WL); set 2 [MY, BW0.75, DIM, and 33 WL (WL selected by BN)]; set 3 (MY, BW0.75, DIM, and fat, protein, and lactose concentrations); set 4 (MY, BW0.75, DIM, 33 WL, fat, protein, and lactose); set 5 (MY, BW0.75, DIM, 33 WL, and visit duration in the feed bunk); set 6 (MY, DIM, and 33 WL); set 7 (MY, BW0.75, and DIM); set-WL (included 361 WL); and set-BN (included just 33 selected WL). All models (i.e., each combination of covariate set and fitting approach, ANN or PLS) were validated with an external data set. The use of ANN improved the performance of models 2, 5, 6, and BN. The use of BN combined with ANN yielded the highest accuracy and precision. The addition of individual WL compared with milk components (set 2 vs. set 3) did not improve prediction quality when using PLS. However, when ANN was employed, the model prediction with the inclusion of 33 WL was improved over the model containing only milk components (set 2 vs. set 3; concordance correlation coefficient = 0.80 vs. 0.72; coefficient of determination = 0.67 vs. 0.53; root mean square error of prediction 2.36 vs. 2.81 kg/d). The use of ANN and the inclusion of a behavior parameter, set 5, resulted in the best predictions compared with all other models (coefficient of determination = 0.70, concordance correlation coefficient = 0.83, root mean square error of prediction = 2.15 kg/d). The addition of milk spectra information to models containing cow variables improved the accuracy and precision of DMI predictions in lactating dairy cows when ANN was used. The use of BN to select more informative WL improved the model prediction when combined with cow variables, with further improvement when combined with ANN.

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows.

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum ß-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83.4% in multiparous cows. These results suggest that predicting serum BHB from continuous test-day milk and performance variables could serve as a valuable diagnostic tool for monitoring HYK in Holstein and Jersey herds.

Urinary purine derivatives as a tool to estimate dry matter intake in cattle: A meta-analysis.

The objectives of this study were to investigate the relationship between dry matter intake (DMI) and urinary purine derivative (PD) excretion, to develop equations to predict DMI and to determine the endogenous excretion of PD for beef and dairy cattle using a meta-analytical approach. To develop the models, 62 published studies for both dairy (45 studies) and beef cattle (17 studies) were compiled. Twenty models were tested using DMI (kg/d) and digestible DMI (dDMI, kg/d) as response variables and PD:creatinine (linear term: PD:C, and quadratic term: PD:C2), allantoin:creatinine (linear term: ALLA:C, and quadratic term: ALLA:C2), metabolic body weight (BW0.75, kg), milk yield (MY, kg/d), and their combination as explanatory variables for dairy and beef (except for MY) cattle. The models developed to predict DMI for dairy cattle were validated using an independent data set from 2 research trials carried out at the University of Wisconsin (trial 1: n = 45; trial 2: n = 50). A second set of models was developed to estimate the endogenous PD excretion. In all evaluated models, the effect of PD (either as PD:C or ALLA:C) was significant, supporting our hypothesis that PD are in fact correlated with DMI. Despite the BW-independent relationship between PD and DMI, the inclusion of BW0.75 in the models with PD:C and ALLA:C as predictors slightly decreased the values of root mean square error (RMSE) and Akaike information criterion for the models of DMI. Our models suggest that both DMI and dDMI can be equally well predicted by PD-related variables; however, predicting DMI seems more useful from a practical and experimental standpoint. The inclusion of MY into the dairy models substantially decreased RMSE and Akaike information criterion values, and further increased the precision of the equations. The model including PD:C, BW0.75, and MY presented greater concordance correlation coefficient (0.93 and 0.63 for trials 1 and 2, respectively) and lower RMSE of prediction (1.90 and 3.35 kg/d for trials 1 and 2, respectively) when tested in the validation data set, emerging as a potentially useful estimator of nutrient intake in dairy cows. Endogenous PD excretion was estimated by the intercept of the linear regression between DMI (g/kg of BW0.75) and PD excretion (mmol/kg of BW0.75) for beef (0.404 mmol/kg of BW0.75) and dairy cattle (0.651 mmol/kg of BW0.75). Based on the very close agreement between our results for beef cattle and the literature, the linear regression appears to be an adequate method to estimate endogenous PD excretion.

Use of milk fatty acids to estimate plasma nonesterified fatty acid concentrations as an indicator of animal energy balance.

Negative energy balance is an important part of the lactation cycle, and measuring the current energy balance of a cow is useful in both applied and research settings. The objectives of this study were (1) to determine if milk fatty acid (FA) proportions were consistently related to plasma nonesterified fatty acids (NEFA); (2) to determine if an individual cow with a measured milk FA profile is above or below a NEFA concentration, (3) to test the universality of the models developed within the University of Wisconsin and US Dairy Forage Research Center cows. Blood samples were collected on the same day as milk sampling from 105 Holstein cows from 3 studies. Plasma NEFA was quantified and a threshold of 600 µEq/L was applied to classify animals above this concentration as having high NEFA (NEFAhigh). Thirty milk FA proportions and 4 milk FA ratios were screened to evaluate their capacity to classify cows with NEFAhigh according to determined milk FA threshold. In addition, 6 linear regression models were created using individual milk FA proportions and ratios. To evaluate the universality of the linear relationship between milk FA and plasma NEFA found in the internal data set, 90 treatment means from 21 papers published in the literature were compiled to test the model predictions. From the 30 screened milk FA, the odd short-chain fatty acids (C7:0, C9:0, C11:0, and C13:0) had sensitivity slightly greater than the other short-chain fatty acids (83.3, 94.8, 80.0, and 85.9%, respectively). The sensitivities for milk FA C6:0, C8:0, C10:0, and C12:0 were 78.8, 85.3, 80.1, and 83.9%, respectively. The threshold values to detect NEFAhigh cows for the last group of milk FA were ≤2.0, ≤0.94, ≤1.4, and ≤1.8 g/100 g of FA, respectively. The milk FA C14:0 and C15:0 had sensitivities of 88.7 and 85.0% and a threshold of ≤6.8 and ≤0.53 g/100 g of FA, respectively. The linear regressions using the milk FA ratios C18:1 to C15:0 and C17:0 to C15:0 presented lower root mean square error (RMSE = 191 and 179 µEq/L, respectively) in comparison with individual milk FA proportions (RMSE = 194 µEq/L), C18:1 to even short-medium-chain fatty acid (C4:0-C12:0) ratio (RMSE = 220 µEq/L), and C18:1 to C14:0 (RMSE = 199 µEq/L). Models using milk FA ratios C18:1 to C15:0 and C17:0 to C15:0 had a better fit with the external data set in comparison with the other models. Plasma NEFA can be predicted by linear regression models using milk FA ratios.

Energy efficiency of growing ram lambs fed concentrate-based diets with different roughage sources.

Poor-quality roughages are widely used as fiber sources in concentrate-based diets for ruminants. Because roughage quality is associated with the efficiency of energy use in forage-based diets, the objective of this study was to determine whether differing the roughage source in concentrate-based diets could change the energy requirements of growing lambs. Eighty-four 1/2 Dorper × 1/2 Santa Inês ram lambs (18.0 ± 3.3 kg BW) were individually penned and divided into 2 groups according to primary source of dietary roughage: low-quality roughage (LQR; sugarcane bagasse) or medium-quality roughage (MQR; coastcross hay). Diets were formulated to be isonitrogenous (2.6% N) and to meet 20% of physically effective NDF. After a 10-d ad libitum adaptation period, 7 lambs from each group were randomly selected and slaughtered (baseline). Twenty-one lambs in each diet group were fed ad libitum and slaughtered at 25, 35, or 45 kg BW. The remaining 28 lambs (14 from each diet group) were submitted to 1 of 2 levels of feed restriction: 70% or 50% of the ad libitum intake. Retentions of body fat, N, and energy were determined. Additionally, 6 ram lambs (44.3 ± 5.6 kg BW) were kept in metabolic cages and used in a 6 × 6 Latin square experiment designed to establish the ME content of the 2 diets at the 3 levels of DM intake. There was no effect of intake level on diet ME content, but it was greater in the diet with LQR than in the diet with MQR (3.18 vs. 2.94 Mcal/kg, respectively; P < 0.01). Lambs fed the diet with LQR had greater body fat (g/kg of empty BW) and energy concentrations (kcal/kg of empty BW) because of a larger visceral fat deposition (P < 0.05). Using a low-quality roughage as a primary source of forage in a concentrate-based diet for growing lambs did not change NEm and the efficiency of ME use for maintenance, which averaged 71.6 kcal/kg(0.75) of shrunk BW and 0.63, respectively. On the other hand, the greater nonfibrous carbohydrate content of the diet with LQR resulted in a 17% better efficiency of ME use for gain (P < 0.01), which was associated with a greater partial efficiency of energy retention as fat (P < 0.01). This increased nutritional efficiency, however, should be viewed with caution because it is related to visceral fat deposition, a nonedible tissue.

Características de fermentação da silagem de seis variedades de milho indicadas para a região semiárida brasileira / Fermentation parameters of silages of six maize varieties recommended for the Brazilian semi-arid region

Avaliaram-se as características fermentativas e a qualidade das silagens de seis variedades de milho, de ciclos precoce e superprecoce - BRS Caatingueiro, BRS Assum Preto, BR 5033 Asa Branca, BR 5028 São Francisco, Gurutuba e BRS 4103 - indicadas para a região semiárida brasileira. Foram utilizados silos experimentais, em delineamento inteiramente ao acaso, com seis tratamentos (variedades) e quatro repetições. Avaliaram-se: matéria seca (MS), matéria orgânica (MO), proteína bruta (PB), fibra em detergente neutro (FDN), fibra em detergente ácido (FDA), extrato etéreo (EE), carboidratos totais (CHO), carboidratos não fibrosos (CNF), pH, nitrogênio amoniacal como parte do nitrogênio total (N-NH3/NT), ácidos orgânicos e digestibilidade in vitro da matéria seca (DIVMS) das silagens. Os valores médios encontrados para a silagem foram: MS= 28,7 por cento; MO= 94,9 por cento; PB= 8,3 por cento; FDN= 49,9 por cento; FDA= 27,5 por cento; EE= 3,8 por cento; CHO= 82,7 por cento; CNF= 32,8 por cento; pH= 3,8; N-NH3/NT= 2,9 por cento/NT; ácido láctico = 7,6 por cento; ácido acético = 0,6 por cento; ácido butírico = 0,3 por cento e DIVMS= 57,9 por cento. As variedades BR 5028 - São Francisco e Gurutuba destacaram-se das demais em relação ao teor de matéria seca. A variedade BRS Caatingueiro apresentou maior teor de carboidratos não fibrosos em relação às demais. As silagens de todas as variedades foram classificadas como de excelente qualidade, por apresentarem potencial para ensilagem no semiárido brasileiro.

The fermentation characteristics and silage quality of six maize varieties of early and super early cycles were evaluated. They are recommended for the Brazilian semi-arid region (BRS Caatingueiro, BRS Assum Preto, BR 5033 - Asa Branca, BR 5028 - São Francisco, Gurutuba and BRS 4103). Experimental silos were used, in a completely randomized design, with six treatments (varieties) and four replicaties. The evaluated parameters were: dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE), total carbohydrates (CHO), non-fibrous carbohydrates (NFC), pH, ammoniacal nitrogen as part of the total nitrogen (N-NH3/TN), organic acids, and in vitro dry matter digestibility (IVDMD) of the silages. The mean values found for silage were: DM= 29.6 percent; OM= 94.9 percent; CP= 8.2 percent; NDF= 49.9 percent; ADF= 27.5 percent; EE= 3.8 percent; CHO= 82.7 percent; NFC= 32.8 percent; pH= 3.8; N-NH3/TN= 2.9 percent/TN; lactic acid = 7.6 percent; acetic acid = 0.6 percent; butyric acid = 0.3 percent; and IVDMD = 57.9 percent. Varieties BR 5028 - São Francisco and Gurutuba stood out (P<0.05) from others in relation to dry matter. The BRS Caatingueiro showed higher (P<0.05) level of non-fiber carbohydrates in relation to the others. The silages from all the varieties were considered of excellent quality, with potential to be conserved as silage in the Brazilian semi-arid.