-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-NH 3 , 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-NH 3 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.Key Words: crude protein, dry matter, effluent, fermentation, gas Avaliação da silagem de capim-elefante com adição de raspa de mandioca RESUMO -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-NH 3 , 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-NH 3 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.Palavras-chave: efluente, fermentaç...

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, 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.

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

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): MONsodium 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.

Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

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 R 2 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 R 2 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.

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 (NEFA). Thirty milk FA proportions and 4 milk FA ratios were screened to evaluate their capacity to classify cows with NEFA 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 NEFA 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.

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.