Search

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.

The current study evaluated growth performance and digestion responses of finishing bulls fed diets containing 825 g/kg flint maize [dry matter (DM) basis] ground to medium (1.66 mm; MG) or coarse particle sizes (2.12 mm; CG), with added monensin (26 mg/kg; DM basis; MON) or a blend of essential oils (BEO) + exogenousα-amylase (AM; 90 mg/kg + 560 mg/kg commercial product, respectively, DM basis). In Expt 1, 256 Nellore bulls were blocked by initial body weight (BW) (360 ± 11.7 kg) and assigned to 48 pens in a 2 × 2 factorial arrangement of treatments. Effect of a maize particle size × feed additive interaction was not detected for final BW, DM intake (DMI), average daily gain (ADG) and feed efficiency. The DMI was greater for bulls fed BEO + AMv.MON. Final BW and ADG tended to be greater for bulls fed CG than MG maize. An interaction was detected for hot carcass weight which was 11 kg heavier for bulls fed BEO + AMv.MON in diets containing CG, but not MG particle size. In Expt 2, four ruminally cannulated Nellore steers were offered the same treatments as Expt 1, in a 4 × 4 Latin Square design. Intake of most nutrients was greater for steers fed CG than steers fed MG maize. In summary, feeding bulls CG maize increased growth performance and carcass characteristics compared with MG. The combination of BEO + AM resulted in heavier carcass weights compared with MON supplementation when included in diets containing CG maize.

Wearable sensors have been explored as an alternative for real-time monitoring of cattle feeding behavior in grazing systems. To evaluate the performance of predictive models such as machine learning (ML) techniques, data cross-validation (CV) approaches are often employed. However, due to data dependencies and confounding effects, poorly performed validation strategies may significantly inflate the prediction quality. In this context, our objective was to evaluate the effect of different CV strategies on the prediction of grazing activities in cattle using wearable sensor (accelerometer) data and ML algorithms. Six Nellore bulls (average live weight of 345 ± 21 kg) had their behavior visually classified as grazing or not-grazing for a period of 15 d. Elastic Net Generalized Linear Model (GLM), Random Forest (RF), and Artificial Neural Network (ANN) were employed to predict grazing activity (grazing or not-grazing) using 3-axis accelerometer data. For each analytical method, three CV strategies were evaluated: holdout, leave-one-animal-out (LOAO), and leave-one-day-out (LODO). Algorithms were trained using similar dataset sizes (holdout: n = 57,862; LOAO: n = 56,786; LODO: n = 56,672). Overall, GLM delivered the worst prediction accuracy (53%) compared with the ML techniques (65% for both RF and ANN), and ANN performed slightly better than RF for LOAO (73%) and LODO (64%) across CV strategies. The holdout yielded the highest nominal accuracy values for all three ML approaches (GLM: 59%, RF: 76%, and ANN: 74%), followed by LODO (GLM: 49%, RF: 61%, and ANN: 63%) and LOAO (GLM: 52%, RF: 57%, and ANN: 57%). With a larger dataset (i.e., more animals and grazing management scenarios), it is expected that accuracy could be increased. Most importantly, the greater prediction accuracy observed for holdout CV may simply indicate a lack of data independence and the presence of carry-over effects from animals and grazing management. Our results suggest that generalizing predictive models to unknown (not used for training) animals or grazing management may incur poor prediction quality. The results highlight the need for using management knowledge to define the validation strategy that is closer to the real-life situation, i.e., the intended application of the predictive model.

Wearable sensors have been adopted as an alternative for real-time monitoring of cattle feeding behavior in grazing systems. However, even using machine learning (ML) techniques confounding effects such as cross-validation strategy may inflate the prediction quality. Our objective was to evaluate the effect of different cross-validation strategies on the prediction of grazing activities in cattle using wearable sensor data and ML algorithms. Six Nellore bulls (345 ± 21 kg) had their behavior visually classified as grazing or not-grazing for a period of 15 days. Generalized Linear Model (GLM), Random Forest (RF), and Artificial Neural Network (ANN) were employed to predict behavior (grazing or not-grazing) using 3-axis accelerometer data. For each analytical method, three cross-validation strategies were evaluated: holdout, leave-one-animal-out (LOAO), and leave-one-day-out (LODO). Algorithms were trained using similar dataset sizes (holdout: n = 57,862; LOAO: n = 56,786; LODO: n = 56,672). Regardless of the cross-validation strategy, GLM achieved the worst prediction accuracy (53%) compared to the ML techniques (65% for both RF and ANN). ANN performed slightly better than RF for LOAO (73%) and LODO (64%) cross-validation strategies. The holdout yielded the highest accuracy values for all three ML approaches (GLM: 59%, RF: 76%, and ANN: 74%), followed by LODO (58%) and LOAO (55%). In conclusion, the GLM approach was not adequate to predict grazing behavior, regardless of the cross-validation strategy. The greater prediction accuracy observed for holdout cross-validation may simply indicate a lack of data independence and the presence of carry-over effects from animals and grazing management. Our results suggest that generalizing predictive models to unknown (not used for training) animals or grazing management may incur in poor prediction quality. The results highlight the need for using biological knowledge to define the validation strategy that is closer to the real-life situation.

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Computer vision systems (CVS) have been shown to be a powerful tool for the measurement of live pig body weight (BW) with no animal stress. With advances in precision farming, it is now possible to evaluate the growth performance of individual pigs more accurately. However, important traits such as muscle and fat deposition can still be evaluated only via ultrasound, computed tomography, or dual-energy x-ray absorptiometry. Therefore, the objectives of this study were: 1) to develop a CVS for prediction of live BW, muscle depth (MD), and back fat (BF) from top view 3D images of finishing pigs and 2) to compare the predictive ability of different approaches, such as traditional multiple linear regression, partial least squares, and machine learning techniques, including elastic networks, artificial neural networks, and deep learning (DL). A dataset containing over 12,000 images from 557 finishing pigs (average BW of 120 ± 12 kg) was split into training and testing sets using a 5-fold cross-validation (CV) technique so that 80% and 20% of the dataset were used for training and testing in each fold. Several image features, such as volume, area, length, widths, heights, polar image descriptors, and polar Fourier transforms, were extracted from the images and used as predictor variables in the different approaches evaluated. In addition, DL image encoders that take raw 3D images as input were also tested. This latter method achieved the best overall performance, with the lowest mean absolute scaled error (MASE) and root mean square error for all traits, and the highest predictive squared correlation (R2). The median predicted MASE achieved by this method was 2.69, 5.02, and 13.56, and R2 of 0.86, 0.50, and 0.45, for BW, MD, and BF, respectively. In conclusion, it was demonstrated that it is possible to successfully predict BW, MD, and BF via CVS on a fully automated setting using 3D images collected in farm conditions. Moreover, DL algorithms simplified and optimized the data analytics workflow, with raw 3D images used as direct inputs, without requiring prior image processing.

With agriculture rapidly becoming a data-driven field, it is imperative to extract useful information from large data collections to optimize the production systems. We compared the efficacy of regression (linear regression or generalized linear regression [GLR] for continuous or categorical outcomes, respectively), random forests (RF) and multilayer neural networks (NN) to predict beef carcass weight (CW), age when finished (AS), fat deposition (FD), and carcass quality (CQ). The data analyzed contained information on over 4 million beef cattle from 5,204 farms, corresponding to 4.3% of Brazil’s national production between 2014 and 2016. Explanatory variables were integrated from different data sources and encompassed animal traits, participation in a technical advising program, nutritional products sold to farms, economic variables related to beef production, month when finished, soil fertility, and climate in the location in which animals were raised. The training set was composed of information collected in 2014 and 2015, while the testing set had information recorded in 2016. After parameter tuning for each algorithm, models were used to predict the testing set. The best model to predict CW and AS was RF (CW: predicted root mean square error = 0.65, R2 = 0.61, and mean absolute error = 0.49; AS: accuracy = 28.7%, Cohen’s kappa coefficient [Kappa] = 0.08). While the best approach for FD and CQ was GLR (accuracy = 45.7%, Kappa = 0.05, and accuracy = 58.7%, Kappa = 0.09, respectively). Across all models, there was a tendency for better performance with RF and regression and worse with NN. Animal category, nutritional plan, cattle sales price, participation in a technical advising program, and climate and soil in which animals were raised were deemed important for prediction of meat production and quality with regression and RF. The development of strategies for prediction of livestock production using real-world large-scale data will be core to projecting future trends and optimizing the allocation of resources at all levels of the production chain, rendering animal production more sustainable. Despite beef cattle production being a complex system, this analysis shows that by integrating different sources of data it is possible to forecast meat production and quality at the national level with moderate-high levels of accuracy.

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.

Computer vision applications in livestock are appealing since they enable measurement of traits of interest without the need to directly interact with the animals. This allows the possibility of multiple measurements of traits of interest with minimal animal stress. In the current study, an automated computer vision system was devised and evaluated for extraction of features of interest, as body measurements and shape descriptors, and prediction of body weight in pigs. From the 655 pigs that had data collected 580 had more than 5 frames recorded and were used for development of the predictive models. The cross-validation for the models developed with data from nursery and finishing pigs presented an R2 ranging from 0.86 (random selected image) to 0.94 (median of images truncated on the third quartile), whereas with the dataset without nursery pigs, the R2 estimates ranged from 0.70 (random selected image) to 0.84 (median of images truncated on the third quartile). However, overall the mean absolute error was lower for the models fitted without data on nursery animals. From the body measures extracted from the image, body volume, area, and length were the most informative for prediction of body weight. The inclusion of the remaining body measurements (width and heights) or shape descriptors to the model promoted significant improvement of the predictions, whereas the further inclusion of sex and line effects were not significant.

Two experiments were conducted to evaluate the performance responses of finishing feedlot cattle to dietary addition of essential oils and exogenous enzymes. The treatments in each experiment consisted of (DM basis): MON-sodium monensin (26 mg/kg); BEO-a blend of essential oils (90 mg/kg); BEO+MON-a blend of essential oils plus monensin (90 mg/kg + 26 mg/kg, respectively); BEO+AM-a blend of essential oils plus exogenous α-amylase (90 mg/kg + 560 mg/kg, respectively); and BEO+AM+PRO-a blend of essential oils plus exogenous α-amylase and exogenous protease (90 mg/kg + 560 mg/kg + 840 mg/kg, respectively). Exp. 1 consisted of a 93-d finishing period using 300 Nellore bulls in a randomized complete block design. Animals fed BEO had higher DMI (P < 0.001) but similar feed efficiency to animals fed MON (P ≥ 0.98). Compared with MON, the combination of BEO+AM resulted in 810 g greater DMI (P = 0.001), 190 g greater average daily gain (P = 0.04), 18 kg heavier final body weight (P = 0.04), and 12 kg heavier hot carcass weight (P = 0.02), although feed efficiency was not significantly different between BEO+AM and MON (P = 0.89). Combining BEO+MON tended to decrease hot carcass weight compared with BEO alone (P = 0.08) but not compared with MON (P = 0.98). Treatments did not impact observed dietary net energy values (P ≥ 0.74) or the observed:expected net energy ratio (P ≥ 0.11). In Exp. 2, five ruminally cannulated Nellore steers were used to evaluate intake, apparent total tract digestibility of nutrients, and ruminal parameters in a 5 × 5 Latin square design. Feeding BEO increased the total tract digestibility of CP compared to MON (P = 0.03). Compared to MON, feeding the combination of BEO+MON increased the intake of CP (P = 0.04) and NDF (P = 0.05), with no effects on total tract digestibility of nutrients (P ≥ 0.56), except for a tendency (P = 0.09) to increase CP digestibility. Intakes of all nutrients measured, except for ether extract (P = 0.16) were greater in animals fed BEO+AM when compared with MON (P ≤ 0.03), with no differences on total tract nutrient digestibilities (P ≥ 0.11) between these two treatments. In summary, diets containing the BEO used herein enhanced DMI of growing-finishing feedlot cattle compared with a basal diet containing MON without impair feed efficiency. A synergism between BEO and AM was detected, further increasing cattle performance and carcass production compared to MON.

In livestock operations, systematically monitoring animal body weight, biometric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves massive datasets. This article aims to provide an overview of how deep learning has been implemented in computer vision systems used in livestock, and how such implementation can be an effective tool to predict animal phenotypes and to accelerate the development of predictive modeling for precise management decisions. First, we reviewed the most recent milestones achieved with computer vision systems and the respective deep learning algorithms implemented in Animal Science studies. Then, we reviewed the published research studies in Animal Science which used deep learning algorithms as the primary analytical strategy for image classification, object detection, object segmentation, and feature extraction. The great number of reviewed articles published in the last few years demonstrates the high interest and rapid development of deep learning algorithms in computer vision systems across livestock species. Deep learning algorithms for computer vision systems, such as Mask R-CNN, Faster R-CNN, YOLO (v3 and v4), DeepLab v3, U-Net and others have been used in Animal Science research studies. Additionally, network architectures such as ResNet, Inception, Xception, and VGG16 have been implemented in several studies across livestock species. The great performance of these deep learning algorithms suggests an improved predictive ability in livestock applications and a faster inference. However, only a few articles fully described the deep learning algorithms and their implementation. Thus, information regarding hyperparameter tuning, pre-trained weights, deep learning backbone, and hierarchical data structure were missing. We summarized peer-reviewed articles by computer vision tasks (image classification, object detection, and object segmentation), deep learning algorithms, animal species, and phenotypes including animal identification and behavior, feed intake, animal body weight, and many others. Understanding the principles of computer vision and the algorithms used for each application is crucial to develop efficient systems in livestock operations. Such development will potentially have a major impact on the livestock industry by predicting real-time and accurate phenotypes, which could be used in the future to improve farm management decisions, breeding programs through high-throughput phenotyping, and optimized data-driven interventions.

Computer vision has been proposed as an alternative to monitor livestock growth, for example using 3D cameras to estimate body weight and condition. However, implementing this technology outdoors and with unrestrained animals poses a great challenge, because of harsh conditions for the equipment, as well as varying lighting and animal movement. Here we propose an automated system to acquire 3D images of individual animals, integrate the animal ID in the image file, and process images. This comprises a fundamental step for implementing a real-time assessment tool of growth of animals grouped in pens, to aid optimal feed management and marketing decision. The study comprised 50 beef cattle of Nellore breed during a finishing phase at DSM Innovation and Applied Center, in Brazil. Animals were tagged with RFID eartags, and 5 Intel RealSense D435 cameras were installed on the top of the water sources in each pen. For each animal approaching the water tank, an RFID antenna read the animal ID and triggered the camera to acquire a top-down view image. Images were then sent to a central computer using Wi-Fi, and later to the cloud. A decision tree algorithm was trained to sort images as usable or unusable, where the retained images had one single animal on the frame, with full body and relatively straight position. Selected frames were then processed using a threshold Otsu’s method to segment the animal body and obtain the necessary biological features, including body length, multiple width and height values, body area and volume. A total of 16,000+ images were collected, from which 3,100 were deemed usable. The algorithm successfully extracted the animal body from each image and obtained the biological features (cross-correlation values above 0.90). The proposed system was demonstrated to be extremely promising and the next stage of the study will be devoted to individual body weight prediction.

In livestock operations, systematically monitoring animal body weight, biometric body measurements, animal behavior, feed bunk, and other complex phenotypes is unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Such technology has emerged as a powerful tool to predict animal identification, body weight, biometric measurements, complex behavioral traits, and feed bunk score. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves massive datasets. The objective of this talk is to provide an overview of how computer vision systems can be an effective tool to integrate animal-level information and to create predictive modeling for precise management decisions. We will discuss some of the challenges, applications, and potentials of computer vision systems in livestock, and some examples to be presented include: (1) monitoring animal growth and behavior; (2) automated feed bunk management; (3) individual animal recognition; and (4) particle size distribution in total mixed ration. The development of computer vision technologies will potentially have a major impact in the livestock industry by predicting real-time and accurate phenotypes, which, in the future, could be used to improve farm management decisions, breeding programs, and to build optimal data-driven interventions.

Currently, the amount of feed delivered daily in feedlots is based on the previous day’s consumption and the estimated animal growth. Nevertheless, factors such as weather variables may influence feed consumption. The objective of this study was to evaluate the effect of weather on dry matter intake (DMI) in beef cattle finished in a commercial feedlot facility in South America. A total of 8.388 animals (Nellore and crossbreed Nellore x Angus) divided across 55 pens were evaluated from July 2018 to April 2019. The animals entered the feedlots with average body weight (BW) of 337 ± 28 kg and had a harvest BW of 517 ± 33 kg, with an average of 125 ± 25 days in feedlot. Daily feed consumption in each pen was estimated by subtracting any morning leftover feed from the total feed amount delivered on the previous day. Data on 16 weather variables for the farm location (latitude and longitude) was acquired using the Dark Sky API and processed in R. The weather variables were clustered via hierarchical cluster into groups given by: G1) humidity, cloud cover, and precipitation; G2) dew point, temperature (max, min, apparent max and apparent min), and day length; G3) atmospheric ozone and pressure; G4) UV index and visibility; G5) wind speed and gust; and G6) moon phase. Variables in G3 were positively correlated with DMI, while G2 and G4 presented negative correlations. The most important correlations were for day length (r = -0.74, P < 0.01), apparent max temperature (r = -0.65, P < 0.05), dew point (r = -0.65, P < 0.05), ozone (r = 0.62,P < 0.05), and pressure (r = 0.61, P < 0.05). In conclusion, several weather variables presented potential as candidate for the development of predictive models of DMI of beef cattle in feedlots.

In this presentation we will discuss our current research on computer vision techniques for optimized management of feed bunks and prediction of live weight in beef cattle. The combination of these two techniques allows not only an enhanced nutritional management in feedlots, but also the determination of economically optimal harvest time for maximized returns. In addition, we will discuss computational and data analytics strategies for integration and analysis of large datasets from multiple sources, including operational farm data, weather and economics, for aiding data-driven decisions to improve beef cattle production.

Made available in DSpace on 2020-12-10T17:27:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-02-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Frequent measurements of body weight (BW) in livestock systems are very important because they allow assessing growth. However, real-time monitoring of animal growth through traditional weighing scales is stressful for animals, costly and labor-intensive. Thus, the objectives of this study were to: 1) assess the predictive quality of an automated computer vision system used to predict BW and average daily gain (ADG) in beef cattle; and 2) compare different predictive approaches, including Multiple Linear Regression (MLR), Least Absolute Shrinkage and Selection Operator (LASSO), Partial Least Squares (PLS), and Artificial Neutral Networks (ANN). A total of 234 images of Nellore beef cattle were collected during the weaning, stocker and feedlot phases. First, biometric body measurements of each animal, such as body volume, area, length, and others, were performed using three-dimensional images captured with the Kinecto (R) sensor, and their respective BW were acquired using an electronic scale. Next, the biometric measurements were used as explanatory variables in the four predictive approaches (MLR, LASSO, PLS, and ANN). To evaluate prediction quality, a leave-one-out cross-validation was adopted. The ANN was the best prediction approach in terms of Root Mean Square Error of Prediction (RMSEP) and squared predictive correlation (r(2)). The results for Weaning were RMSEP = 8.6 kg and r(2) = 0.91; for Stocker phase, RMSEP = 11.4 kg and r(2) = 0.79; and for Beginning of feedlot, RMSEP = 7.7 kg and r(2) = 0.92. The ANN was also the best method for prediction of ADG, with RMSEP = 0.02 kg/d and r(2) = 0.67 for the period between Weaning and Stocker, RMSEP = 0.02 kg/d and r(2) = 0.85 for the Weaning and Beginning of Feedlot phase, RMSEP = 0.03 kg/d and r(2) = 0.80 for Weaning and Final of Feedlot phase, RMSEP = 0.10 kg/d and r(2) = 0.51 for Stocker and Beginning of feedlot phase, and RMSEP = 0.09 kg/d and r(2) = 0.82 for the Beginning and Final of feedlot phase. Overall, the results indicate that the proposed automated computer vision system can be successfully used to predict BW and ADG in real-time in beef cattle. Univ Wisconsin, Dept Anim Sci, Madison, WI 53706 USA Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53706 USA Sao Paulo State Univ, Sch Vet Med & Anim Sci, BR-18618681 Botucatu, SP, Brazil Sao Paulo State Univ, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, Brazil Univ Fed Lavras, Anim Sci Dept, BR-3720000 Lavras, MG, Brazil Univ Fed Triangulo Mineiro, BR-38280000 Iturama, MG, Brazil Sao Paulo State Univ, Sch Vet Med & Anim Sci, BR-18618681 Botucatu, SP, Brazil Sao Paulo State Univ, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, Brazil FAPESP: 2017/20812-0 FAPESP: 2017/02057-0 CAPES: 001

Context Cattle grazing tropical forages usually perform below genetic potential due to limited nutrient intake. Aims Four experiments were conducted to evaluate supplementation strategies on performance and metabolism of cattle grazing intensively managed marandu palisade grass (Urochloa brizantha). Methods Experiment 1 evaluated the average daily gain (ADG) of 72 young bulls (222 ± 25 kg bodyweight, BW) grazing palisade grass and supplemented (22% crude protein, CP) at 0.0%, 0.3%, 0.6% and 0.9% BW, and their ADG during the feedlot finishing phase. Experiment 2 evaluated the ADG of 80 bulls (240 ± 18 kg BW) grazing palisade grass and supplemented with energy (11.3% CP) or three protein sources (≈20.5% CP) at 0.6% BW. Experiment 3 investigated intake, rumen parameters and digestibility of nutrients in fistulated steers (410 ± 8.6 kg BW) fed an energy supplement, that is, ground corn, at 0.0%, 0.3%, 0.6% and 0.9% BW, with a parallel in vitro study of fermentation kinetics (Experiment 4). Key results Increased levels of supplementation resulted in linear increases (P < 0.05) in ADG, stocking rate (SR) and in BW gain per area. There were no differences (P > 0.05) in ADG, SR and BW gain per area among supplemental sources of protein or the energy supplement. Increasing energy levels caused a linear decrease (P < 0.05) in forage intake and grazing time and a linear increase (P < 0.05) in total dry matter and digestible-nutrient intakes, but did not affect (P > 0.05) fibre degradability. Corn supplementation also caused a linear decrease (P < 0.05) in acetate:propionate ratio, in ruminal ammonia-N and in N excretion, and a linear increase (P < 0.05) in rumen propionate concentration, in microbial synthesis and in N retention. The supplementation increased BW at the start of the feedlot phase, resulting in similar hot carcass weights with fewer days on feed and no effects on meat quality. Conclusions Overall, despite the source utilised, supplementation increased ADG, SR and BW gain per area, with fewer days being required on the finishing period. Implications Having adequate supplementation strategies in place will help producers increase the efficiency of their systems.

The objective of this study was to investigate the effects of energy supplementation and pre-grazing sward height on grazing behavior, nutrient intake, digestion, and metabolism of cattle in tropical pastures managed as a rotational grazing system. Eight rumen-cannulated Nellore steers (24 mo of age; 300 ± 6.0 kg body weight [BW]) were used in a replicated 4 × 4 Latin square design. Treatments consisted of two levels of energy supplementation (0% [none] or 0.3% of BW of ground corn on an as-fed basis) and two pre-grazing sward heights (25 cm [defined by 95% light interception (LI)] or 35 cm [defined by ≥ 97.5% LI]) constituting four treatments. Steers grazed Marandu Palisadegrass [Brachiaria brizantha Stapf. cv. Marandu] and post-grazing sward height was 15 cm for all treatments. Forage dry matter intake (DMI) was increased (P = 0.01) when sward height was 25 cm (1.86% vs. 1.32% BW) and decreased (P = 0.04) when 0.3% BW supplement was fed (1.79% vs. 1.38% BW). Total and digestible DMI were not affected by energy supplementation (P = 0.57) but were increased when sward height was 25 cm (P = 0.01). Steers grazing the 25-cm sward height treatment spent less time grazing and more time resting, took fewer steps between feeding stations, and had a greater bite rate compared with 35-cm height treatment (P < 0.05). Energy supplementation reduced grazing time (P = 0.02) but did not affect any other grazing behavior parameter (P = 0.11). Energy supplementation increased (P < 0.01) diet dry matter digestibility but had no effect on crude protein and neutral detergent fiber digestibilities (P = 0.13). Compared with 35-cm pre-grazing sward height, steers at 25 cm presented lower rumen pH (6.39 vs. 6.52) and greater rumen ammonia nitrogen (11.22 vs. 9.77 mg/dL) and N retention (49.7% vs. 20.8%, P < 0.05). The pre-grazing sward height of 25 cm improved harvesting efficiency and energy intake by cattle, while feeding 0.3% of BW energy supplement did not increase the energy intake of cattle on tropical pasture under rotational grazing.

Frequent measurements of body weight (BW) in livestock production systems are very important because they allow the assessment of growth development of animals. However, monitoring animal growth through traditional weighing scales is laborious and stressful for animals. Thus, the objectives of this study were to: 1) assess the predictive quality of an automated computer vision system used to predict BW and average daily gain (ADG) in beef cattle; and 2) compare different predictive approaches (Multiple Linear Regression: MLR, Least Absolute Shrinkage and Selection Operator: LASSO, Partial Least Squares: PLS, and Artificial Neutral Networks: ANN). A total of 234 images of Nellore beef cattle were collected during weaning, stocker and feedlot phase. Biometric body measurements from each animal were performed using 3D images captured with the Kinect® sensor, together with their respective BW acquired using an electronic scale. The biometric measurements were used as explanatory variables for each predictive model. Prediction quality was assessed using a leave-one-out cross-validation strategy. The ANN approach resulted on higher precision and accuracy for BW prediction compared to the other methods, with Root Mean Square Error of Prediction (RMSEP) and squared predictive correlation (r2) equal to: RMSEP = 8.6 kg and r2= 0.91 for weaning; RMSEP = 11.4 kg and r2= 0.79 for stocker, and RMSEP = 7.7 kg and r2= 0.92 for beginning of feedlot. The ANN was also superior for prediction of ADG for the weaning to stocker, weaning to beginning of feedlot, weaning to end of feedlot, stocker to beginning of feedlot and beginning to end of feedlot, with RMSEP: 0.02, 0.02, 0.03, 0.10 and 0.09 kg/d, and r2: 0.67, 0.85, 0.80, 0.51 and 0.82, respectively. Overall, results indicate that an automated computer vision system is a potential tool for real-time measurement of BW and ADG in beef cattle.

The measurement of carcass traits in live pigs, such as muscle depth (MD) and backfat thickness (BF), is a topic of great interest for breeding companies and production farms. Breeding companies currently measure MD and BF using medical imaging technologies such as ultrasound (US). However, US is costly, requires trained personnel, and involves direct interaction with the animals, which is an added stressor. An interesting alternative in this regard is to use computer vision techniques. Farmers would also take advantage of such an application as they would be able to better adjust feed composition and delivery. Therefore, the objectives of this study were: (1) to develop a computer vision system for prediction of MD and BF from 3D images of finishing pigs; (2) to compare the predictive ability of statistical (multiple linear regression, partial least squares) and machine learning (elastic networks and artificial neural networks) approaches using features extracted from the images against a deep learning (DL) approach that uses the raw image as input. A dataset containing 3D images and ultrasound measurements of 618 pigs with average body weight of 120 kg, MD of 65 mm, and BF of 6 mm was used in this study. To assess the predictive performance of the different strategies, a 5-fold cross-validation approach was used. The DL achieved the best predictive performance for both traits, with predictive mean absolute scaled error (MASE) of 5.10% and 13.62%, root-mean-square error (RMSE) of 4.35mm and 1.10mm, and R2 of 0.51 and 0.45, for MD and BF respectively. In conclusion, it was demonstrated that it is possible to satisfactorily predict MD and BF using 3D images that were autonomously collected in farm conditions. Also, the best predictive quality was achieved by a DL approach, simplifying the data workflow as it uses raw 3D images as inputs.

SUMMARY The objectives were to evaluate the performance of bull calves under two grazing strategies and the use of energy supplementation in a rotational system of marandu palisade grass (Urochloa brizantha) and to assess the morphological and chemical composition of the grass. Eighty eight-month-old bulls of 224 ± 2.4 kg body weight (BW) were used in a completely randomized block design in a 2×2 factorial arrangement of two post-grazing heights (i.e.10 cm or 15 cm) and of a citrus pulp-based supplement (i.e. 72% total digestible nutrients) fed daily at 0 or 6 g/kg BW. Initial grazing height was set at 25 cm with variable grazing intervals and stocking rate adjustments used to control the grazing heights. Statistical analyses were performed using the SAS Mixed procedure. Significance was set at 0.05. Forage data from 12 pre-determined paddocks showed no differences in forage mass (FM) and morphological composition at pre-grazing, but greater post-grazing FM and shorter grazing interval in pastures managed at 15 cm. Higher grazing pressure resulted in lowest BW gains for non-supplemented bulls in the 10 cm treatment. The use of an energy supplement and 15 cm postgrazing height resulted in the greatest BW gains; however, combination of10 cm post-grazing height and energy supplementation allowed greater stocking rates. Under high grazing pressure, supplementary feed overcame the normal limitations, and high gains were achieved.

Computer vision systems (CVS) have many applications in livestock, for example, they allow measuring traits of interest without the need for directly handling the animals, avoiding unnecessary animal stress. The objective in the current study was to devise an automated CVS for extraction of variables as body measurements and shape descriptors in pigs using depth images. These features were then tested as potential predictors of live body weight (BW) using a 5-fold cross validation (CV) with different modeling approaches: traditional multiple linear regression (LR), partial least squares (PLS), elastic networks (EL), and artificial neural networks (ANN). The devised CVS could analyze and extract features from a video fed at a rate of 12 frames per second. This resulted in a dataset with more than 32 thousand frames from 655 pigs. However, only the 580 pigs with more than 5 frames recorded were used for the development of the predictive models. From the body measures extracted from the images, body volume, area and length presented the highest correlations with BW, while widths and heights were highly correlated with each other (Figure 1). The results of the CV of the models developed for predictions of BW using a selected set of the more significant variables presented mean absolute errors (MAE) of 3.92, 3.78, 3.72, and 2.57 for PLS, LR, EN, and ANN respectively (Table 1). In conclusion, the CVS developed can automatically extract relevant variables from 3D images and a fully connected ANN with 6 hidden layers presented the overall best predictive results for BW.

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, BW 0.75 , DIM, and 361 WL); set 2 [MY, BW 0.75 , DIM, and 33 WL (WL selected by BN)]; set 3 (MY, BW 0.75 , DIM, and fat, protein, and lactose concentrations); set 4 (MY, BW 0.75 , DIM, 33 WL, fat, protein, and lactose); set 5 (MY, BW 0.75 , DIM, 33 WL, and visit duration in the feed bunk); set 6 (MY, DIM, and 33 WL); set 7 (MY, BW 0.75 , 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.

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 β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerod, 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.

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.

The advent of fully automated data recording technologies and high-throughput phenotyping (HTP) systems has opened up a myriad of opportunities to advance breeding programs and livestock husbandry. Such technologies allow scoring large number of animals for novel phenotypes and indicator traits to boost genetic improvement, as well as for real-time monitoring of animal behavior and development for optimized management decisions. HTP tools include, for example, image analysis and computer vision, sensor technology for motion, sound and chemical composition, and spectroscopy. Applications span from health surveillance, precision nutrition, and control of meat and milk composition and quality. However, the application of HTP requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves large datasets with many covariates and complex relationships. In this talk we will discuss some of the challenges and potentials of HTP in livestock. Some examples to be presented include the utilization of automated feeders to record feed intake and to monitor feeding behavior in broilers, milk-spectra information to predict dairy cattle feed intake, and image analysis and computer vision to monitor growth and body condition in pigs and cattle. HTP and big data will become an essential component of modern livestock operations in the context of precision animal agriculture, boosting animal welfare, environmental footprint, and overall sustainability of animal production.

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 Ines 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.

The objective of this study was to evaluate in situ ruminal degradation kinetics in two experiments, which different levels of dehydrated jackfruit (0, 5, 10 and 15% in natural matter) and cassavas scraping (0, 7, 15 and 30 % in natural matter) in the elephant grass silage, were used. The two experiments were carried out at the Department of Animal Science, Federal University of Viçosa, Brazil, in the same period and followed the same protocol. A mixed model was used, in which was considered random effect of experiment and fixed effects of experimental treatment and interaction among treatments with experiments. Samples of 3 g of each silage treatment were incubated in the rumen of three steers by periods of 0, 3, 6, 12, 24, 36, 48, 72, 96 and 120 hours. Then, the potentially degradable fraction(B), indigestible fraction (I), degradation rate of potentially degradable fraction (c), lag time, and effective degradability (ED) at 2, 5 and 8% per hour, were evaluated. No effect of addition of additives in ensiling of Napier grass for the effective degradability (P> 0.05) was observed, presenting an intercept at 47.7, 40.7 and 34.9% for the passage rates of 2, 5 and 8 %/h respectively. There were significant interaction among experiments with treatments (P Objetivou-se com este estudo avaliar a cinética de degradação ruminal in situ em dois experimentos, os quais utilizaram níveis de jaca desidratada (0, 5, 10 e 15% na matéria natural) e raspa de mandioca (0, 7, 15 e 30% na matéria natural) na ensilagem do capim elefante. Os dois experimentos foram realizados no Departamento de Zootecnia da Universidade Federal de Viçosa, no mesmo período e seguiram o mesmo protocolo. Foi utilizado um modelo misto no qual foi considerado o efeito aleatório de experimento e efeitos fixos de tratamento e interação tratamento com experimento. Amostras de 3 g de silagens de cada tratamento foram incubadas no rúmen de três novilhas por períodos de 0, 3, 6, 12, 24, 36, 48, 72, 96 e 120 horas. Em seguida avaliou-se a fração potencialmente degradável (B), a fração indigestível (I), taxa de degradação da fração potencialmente degradável (c), lag time, e degradabilidade efetiva (DE) a 2, 5 e 8%/hora. Não houve efeito da inclusão de aditivos na ensilagem do capim elefante sobre a degradabilidade efetiva (P>0,05), apresentando intercepto em 47,7; 40,7 e 34,9% para as taxas de passagem de 2, 5 e 8%/h, respectivamente. Houve efeito da interação experimentos com tratamentos (P

Avaliaram-se os efeitos da inclusão de fubá de milho (5%) e uréia (1%) sobre a composição bromatológica e o perfil fermentativo da silagem de jaca, utilizando-se delineamento experimental inteiramente casualisado em arranjo fatorial 3x7 (tratamentos x dia de abertura), com duas repetições. Foram utilizados silos experimentais de PVC, 10 cm de diâmetros x 40 cm de comprimento. A abertura dos silos seguida da avaliação da composição bromatológica e do perfil fermentativo da silagem ocorreu aos 1, 3, 5, 7, 14, 28 e 56 dias após a ensilagem. Os teores de matéria seca (MS) e proteína bruta (PB) foram elevados significativamente (P

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.

Fermentation kinetics rates of co-products generated in the supply chain of biodiesel were evaluated by in vitro gas production technique. Crambe meal, cottonseed meal, crambe cake, soybean cake and sunflower cake were evaluated. The soybean cake had higher rates of degradation of non-fiber carbohydrates (NFC), compared to other foods, resulting in higher volume of gas. Cottonseed meal and crambe meal had degradation rates of NFC and gas production similar. Although the crambe cake did not differ from crambe meal on gas production in the degradation of NFC, there were differences in the rate of degradation, being the highest value found for the cake. The biggest lag time was required for soybean cake and the lowest for the sunflower cake. The highest rates of degradation of fibrous carbohydrates (FC) were observed in soybean cake and crambe cake, and lowest in sunflower cake. The highest gas production coming from the degradation of FC was obtained for the crambe meal. The soybean cake and crambe cake were the co-products with a better profile on rumen fermentation kinetics in relation to the degradation of non-fiber carbohydrates and fibrous carbohydrates.Objetivou-se com este experimento avaliar a cinética de fermentação ruminal de diferentes co-produtos gerados na cadeia produtiva do biodiesel, mediante o uso da técnica de semi-automática de produção de gases in vitro, usando um delineamento inteiramente casualizado, com cinco tratamentos e quatro repetições por tratamento. Os tratamentos foram: farelo de crambe; farelo de algodão; torta de crambe; torta de soja e torta de girassol. A torta de soja apresentou maior taxa de degradação de carboidratos não fibrosos (CNF), quando comparada aos demais alimentos, resultando em maior volume final de gases. O farelo de algodão e o farelo de crambe apresentaram taxa de degradação de CNF e produção de gases similares. Apesar da torta de crambe não diferir do farelo de crambe quanto a produção de gás na degradação de CNF, houve diferença na taxa de degradação, sendo o maior valor verificado para a torta. O maior tempo de colonização (Lag time) foi requerido para a torta de soja e o menor para a torta de girassol. As maiores taxas de degradação de carboidratos fibrosos (CF) foram observadas na torta de soja e torta de crambe, e menor taxa na torta de girassol. A maior produção de gás oriunda da degradação de CF foi obtida para o farelo de crambe. A torta de soja e a torta de crambe foram os co-produtos com melhor perfil na cinética de fermentação ruminal no que diz respeito à degradação de carboidratos não fibrosos e carboidratos fibrosos.

The objective of this experiment was to evaluate the effects of adding cassava scrapings on gas and effluent losses, dry matter recovery, pH, contents of N-NH3, organic acids and volatile fatty acids and the bromatological composition of elephant grass silages. It was used a randomized complete design, with four levels of cassava scrapings (0, 7, 15 or 30% natural matter) each one with four replications per level. The grass was cut at 50 days of regrowth and ensiled in 15-L silos, equipped with a Bunsen valve to allow gas outflow. The gas losses decreased quadratically with the addition of cassava scrapings, whereas effluent losses decreased linearly. Dry matter recovery increased quadratically with the addition of cassava scrapings. Dry matter (DM) concentration increased but crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and hemicellulose (HEM) decreased linearly with the addition of cassava scrapings. The pH value and lactic acid concentration increased quadratically with the addition of cassava scrapings. Contents of N-NH3 and butyric acid decreased quadratically with the addition of cassava scrapings, whereas acetic acid content decreased linearly. Addition of cassava scrapings reduced gas and effluent losses and improved the fermentation profile of elephant grass silages and the level of 7% already ensures this improvement. Objetivou-se com este experimento avaliar os efeitos da adição de raspa de mandioca na ensilagem sobre as perdas por gases e efluentes, a recuperação da matéria seca, o pH, os teores de N-NH3, ácidos orgânicos e ácidos graxos voláteis e a composição bromatológica de silagens de capim-elefante. Utilizou-se um delineamento inteiramente casualizado, com quatro níveis de raspa de mandioca (0, 7, 15 ou 30% da matéria natural), cada um com quatro repetições. O capim foi cortado aos 50 dias de rebrota e ensilado em silos de 15 litros de capacidade, com válvula de bunsen para escape dos gases. A adição de raspa de mandioca ocasionou redução quadrática nas perdas por gases e redução linear nas perdas por efluente. A recuperação de matéria seca aumentou de forma quadrática com a adição de raspa de mandioca. O teor de matéria seca aumentou, enquanto os de proteína bruta, fibra em detergente neutro, fibra em detergente ácido (FDA) e hemicelulose diminuíram linearmente com a adição de raspa de mandioca. O valor de pH e o teor de ácido lático aumentaram de forma quadrática com a adição de raspa de mandioca. Os teores de N-NH3 e de ácido butírico diminuíram de forma quadrática com a adição de raspa de mandioca, enquanto o teor de ácido acético diminuiu linearmente. A inclusão de raspa de mandioca na ensilagem reduz as perdas por gases e efluentes e melhora o perfil fermentativo de silagens de capim-elefante e o nível de 7% da matéria natural é suficiente para assegurar essa melhora.

Rations with higher concentrate content lead to greater average daily, better feed to gain ratio, greater fat deposition on carcasses, increased dressing percentage and lower operating costs in feedlot, making the activity more profitable (Preston 1998; Nunez et al. 2008; Carareto et al. 2010). Because of these reasons and the high cost of energy of preserved forage, worldwide concentrate-rich diets have been studied in the countries that have significant production of beef and milk in feedlots (Santos et al. 2011). In general, cereal grains represent the main energy source in rations for cattle finished in feedlot (Huntington 1997; Owens et al. 1997; Santos et al. 2011).

O estudo teve como objetivo avaliar os efeitos de diferentes níveis de suplementação energética sobre os parâmetros metabólicos (ruminais e sanguíneos), consumo de forragem, comportamento animal e cinética de fermentação ruminal de novilhos Nelore recriados em pastagem tropical na época das águas. Os tratamentos foram: T1) controle (somente suplementação mineral); T2) 0,3% PC; T3) 0,6% PC; e T4) 0,9% PC em milho moído fino e monensina sódica. Foram utilizados 8 novilhos Nelore, fistulados no rúmen, com aproximadamente 410 kg e 48 meses de idade. O delineamento estatístico utilizado foi o de quadrado latino, com 2 quadrados latinos 4x4. Os animais foram alocados em 1 ha de pastagem de capim Brachiaria brizantha cv. Marandu. Os parâmetros avaliados foram o consumo de forragem, comportamento animal, pH e amônia ruminais, concentrações de ácidos graxos de cadeia curta (AGCC), teores plasmáticos de glicose e uréia, cinética de degradação ruminal, síntese de proteína microbiana e excreção de nitrogênio. Dois marcadores foram testados para estimar o consumo, LIPE e óxido crômico, e o escolhido para estimar o consumo foi o óxido crômico. A suplementação energética reduziu linearmente (P

Objetivou-se com este estudo avaliar a cinética de degradação ruminal in situ em dois experimentos, os quais utilizaram níveis de jaca desidratada (0, 5, 10 e 15% na matéria natural) e raspa de mandioca (0, 7, 15 e 30% na matéria natural) na ensilagem do capim elefante. Os dois experimentos foram realizados no Departamento de Zootecnia da Universidade Federal de Viçosa, no mesmo período e seguiram o mesmo protocolo. Foi utilizado um modelo misto no qual foi considerado o efeito aleatório de experimento e efeitos fixos de tratamento e interação tratamento com experimento. Amostras de 3 g de silagens de cada tratamento foram incubadas no rúmen de três novilhas por períodos de 0, 3, 6, 12, 24, 36, 48, 72, 96 e 120 horas. Em seguida avaliou-se a fração potencialmente degradável (B), a fração indigestível (I), taxa de degradação da fração potencialmente degradável (c), lag time, e degradabilidade efetiva (DE) a 2, 5 e 8%/hora. Não houve efeito da inclusão de aditivos na ensilagem do capim elefante sobre a degradabilidade efetiva (P>0,05), apresentando intercepto em 47,7; 40,7 e 34,9% para as taxas de passagem de 2, 5 e 8%/h, respectivamente. Houve efeito da interação experimentos com tratamentos (P 0.05) was observed, presenting an intercept at 47.7, 40.7 and 34.9% for the passage rates of 2, 5 and 8 %/h respectively. There were significant interaction among experiments with treatments (P