Invited review: Methods to determine body fat reserves in the dairy cow with special regard to ultrasonographic measurement of backfat thickness

As the dairy cow uses body energy reserves in early lactation, body condition scoring has become an integral part of dairy herd management. Several methods based on visual and tactile evaluation have been developed. Problems caused by the subjectivity of these techniques have been reported. Alternative approaches to predict energy reserves or energy balance in dairy cattle include metabolic profiling and measurement of live weight, heart girth, or skinfold thickness. A less common method to assess fat reserves in body tissues is measuring backfat thickness (BFT) by using ultrasound. An ultrasound technique has been established to predict carcass quality in beef cattle. A new aspect is the application of ultrasound as a monitoring tool in dairy herd management where another location has to be evaluated. This technique has been validated by relating BFT to total body fat (TBF) content and carcass BFT. Backfat thickness also has been related to other methods of body condition scoring. Target values for the development of BFT throughout lactation are available. The relationship between BFT and TBF content is highly significant although biased by multiple factors. A change in BFT of 1 mm equates to approximately 5 kg of TBF content. Measuring BFT by ultrasound is of added value compared with other body condition scoring systems because it is objective and precise. Changes in body condition can be detected and evaluated properly.     

Thin and fat cows,and the nonlinear genetic relationship between body condition score and fertility

Thin and fat cows are often credited for low fertility, but body condition score (BCS) has been traditionally treated as a linear trait when genetic correlations with reproductive performance have been estimated. The aims of this study were to assess genetic parameters for fertility, production, and body condition traits in the Brown Swiss population reared in the Alps (Bolzano-Bozen Province, Italy), and to investigate the possible nonlinearity among BCS and other traits by analyzing fat and thin cows. Records of BCS measured on a 5-point scale were preadjusted for year-season and days in milk at scoring, and were considered positive (1) for fat cows if they exceeded the value of 1 residual standard deviation or null (0) otherwise, whereas positive values for thin cows were imputed to records below −1 residual standard deviation. Fertility indicators measured on first- and second-parity cows were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield, lactation milk yield, and lactation length. Data were from 1,413 herds and included 16,324 records of BCS, fertility, and production for first-parity, and 10,086 fertility records for second-parity cows. Animals calved from 2002 to 2007 and were progeny of 420 artificial insemination bulls. Genetic parameters for the aforementioned traits were obtained under univariate and bivariate threshold and censored linear sire models implemented in a Bayesian framework. Posterior means of heritabilities for BCS, fat cows, and thin cows were 0.141, 0.122, and 0.115, respectively. Genetic correlations of body condition traits with contemporary production were moderate to high and were between −0.556 and 0.623. Body condition score was moderately related to fertility in first (−0.280 to 0.497) and second (−0.392 to 0.248) lactation. The fat cow trait was scarcely related to fertility, particularly in first-parity cows (−0.203 to 0.281). Finally, the genetic relationships between thin cows and fertility were higher than those between BCS and fertility, both in first (−0.456 to 0.431) and second (−0.335 to 0.524) lactation. Body condition score can be considered a predictor of fertility, and it could be included in evaluation either as linear measure or as thin cow. In the second case, the genetic relationship with fertility was stronger, exacerbating the poorest body condition and considering the possible nonlinearity between fertility and energy reserves of the cow.     

Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.     

Effect of prepartum dietary energy on condition score, postpartum energy, nitrogen partitions, and lactation production responses

Objectives were to examine the effects of feeding to alter body condition at calving on subsequent full lactation production performance and feed intake, on BW and periparturient blood traits, and on complete energy and N balances and ration digestibility during wk 6, 10, and 14 postpartum. Thirty pluriparous Holstein cows were assigned randomly to two energy intakes from wk 33 of previous lactation through the dry period to create either normal (7.2) or thin (5.8) mean body condition scores at calving (9 = fat, 1 = thin). The thin group was fed 0 kg hominy feed daily; the normal group was fed 2.7 kg daily to supplement forage DM available ad libitum during this period. When compared with the normal group, cows in the thin condition group exhibited less negative body fat balance (-206 vs. -507 g/d); similar milk yield, DM intake, N partitions, and nutrient digestibilities; and lower fat test (3.2 vs. 4.1%) during the balance measurements. Whole blood and serum traits were within normal physiological ranges. Full lactation measurements were similar between treatments except that milk fat percentage was lower and DM intake (as percentage of BW), was higher in the thin condition group. Although mean BW at calving was more (651 vs. 599 kg) for normal condition cows, condition scores and BW were not significantly different at 14 wk postpartum; BW curves indicated similar rates of recovery of weight thereafter. Cows considered underconditioned at parturition mobilized less body fat after calving, resulting in reduced milk fat concentration without significant effects on milk yield, protein, SNF, DM intake, or nutrient utilization.     

基于自动测体的围度曲线拟合模型

通过拍摄人体正面和侧面的图像,提取其轮廓线形用于服装三维人体尺寸的自动测量技术。该技术成本低,转换后二维尺寸的计算简单快速,适合产品的商业化。（一般的计算方法是对人体围度进行样本分析,利用拟合方法获得不同号型样本的围度曲线函数模型,通过围度曲线的二维信息估计尺寸。）主要基于自动人体测量需要,在选取号型随机样本的试验基础上,使用指数曲线、对数曲线、二次曲线和三次曲线拟合其中胸围曲线,建立对应的围度函数模型并对其在误差和线形方面展开详细的分析与比较,论证这些模型的合理性与不足之处,为建立更适用的围度尺寸函数模型提供参考。     

Classification and longitudinal examination of callused teat ends in dairy cows

To examine the development of teat end callosity thickness and roughness in early lactation and to quantify cow factors of interest, a system to classify teat end condition was developed. A distinction was made between rough and smooth rings around the teat orifice. In addition, a classification of the degree of callosity was developed. Kappa coefficients for the repeatability of scoring by this classification system by different workers were 0.71 for teat end callosity thickness and 0.86 for teat end callosity roughness. The teat end callosity classification system was used for a longitudinal study with 40 cows during the first 14 wk of lactation. Models were built to predict teat end callosity thickness and roughness, machine-on time, and milk yield. For the response variables, teat end callosity thickness, machine-on time, and milk yield, the consecutive measurements appeared to follow a lactation curve model with a subject-specific general slope and intercept. Teat end callosity increased rapidly the first 8 wk. Cow factors such as days in milk, parity, machine-on time, and teat end shape were associated with the degree of teat end callosity, and the probability of the callosity ring to become rough. Teat end callosity thickness did not decrease within the 14-wk trial period for most teats. Pointed or round teat ends showed more callus than inverted teat ends. Longer machine-on time resulted in a higher probability of the callosity ring to become rough. Rear teats showed less callosity than front teats in this study.     

Invited review: Assessment of body condition score and body fat reserves in relation to insulin sensitivity and metabolic phenotyping in dairy cows

The purpose of this article is to review body condition scoring and the role of body fat reserves in relation to insulin sensitivity and metabolic phenotyping. This article summarizes body condition scoring assessment methods and the differences between subcutaneous and visceral fat depots in dairy cows. The mass of subcutaneous and visceral adipose tissue (AT) changes significantly during the transition period; however, metabolism and intensity of lipolysis differ between subcutaneous and visceral AT depots of dairy cows. The majority of studies on AT have focused on subcutaneous AT, and few have explored visceral AT using noninvasive methods. In this systematic review, we summarize the relationship between body fat reserves and insulin sensitivity and integrate omics research (e.g., metabolomics, proteomics, lipidomics) for metabolic phenotyping of cows, particularly overconditioned cows. Several studies have shown that AT insulin resistance develops during the prepartum period, especially in overconditioned cows. We discuss the role of AT lipolysis, fatty acid oxidation, mitochondrial function, acylcarnitines, and lipid insulin antagonists, including ceramide and glycerophospholipids, in cows with different body condition scoring. Nonoptimal body conditions (under- or overconditioned cows) exhibit marked abnormalities in metabolic and endocrine function. Overall, reducing the number of cows with nonoptimal body conditions in herds seems to be the most practical solution to improve profitability, and dairy farmers should adjust their management practices accordingly.     

Protein and fat mobilization and associations with serum β-hydroxybutyrate concentrations in dairy cows

The objective of this study was to obtain information on variation between dairy cows in muscle and fat tissue mobilization around parturition and to study the association between protein and fat mobilization and serum β-hydroxybutyrate (BHBA) concentrations (hyperketonemia) in this period. Thirty-four cows kept under similar conditions at a university dairy farm (no experimental treatments) were monitored from 4 wk before until 8 wk after calving. Mobilization of muscle protein was investigated by analysis of plasma 3-methylhistidine concentrations (3-MH, analyzed by a recently developed HPLC tandem mass spectrometry method) and ultrasound measurements of longissimus muscle thickness. Mobilization of fat tissue was monitored by serum nonesterified fatty acid (NEFA) concentrations and ultrasound measurements of backfat thickness. Large variation was observed between cows in onset and duration of periparturient protein and fat mobilization. Plasma 3-MH concentrations and muscle thickness profiles indicated that protein mobilization started, on average, before parturition and continued until approximately wk 4 of lactation. Serum NEFA concentrations and backfat thickness profiles showed that fat mobilization occurred from parturition until the end of the study. Thus, muscle protein mobilization occurred in advance of fat mobilization in most cows from this study. We hypothesized that this might be due to a prepartum amino acid deficiency in the absence of negative energy balance. The incidence of hyperketonemia in this study was 16/34 = 47%. With the exception of 3 cows defined as having severe hyperketonemia, cows with lower 3-MH concentrations had higher serum BHBA concentrations. A possible explanation for this observation might be that higher mobilization of protein around calving might restrict ketone body production due to the higher availability of glucogenic precursors in the period of most severe negative energy balance and highest fat mobilization. The validity of this hypothesis needs to be confirmed, but data from this study indicate that further research on the role of protein mobilization in the etiology of hyperketonemia in dairy cows is needed.     

Use of linear measurements of m. longissimus to predict the muscle content of beef carcasses

Five linear measurements associated with the eye muscle (m. longissimus), together with hot carcass weight, 10th rib fat thickness, eye muscle area and an estimate of eye muscle volume (eye muscle area × a carcass length measurement) were made on 53 chilled beef carcasses (hot weight 143-384 kg). The right side of each carcass was anatomically dissected into muscle, bone, fat and connective tissue. Correlation and regression analyses were used to identify the most accurate predictors of weight and percentage of side muscle. In simple regression, hot carcass weight and the estimate of eye muscle volume were the most accurate predictors of side muscle weight; 10th rib fat thickness and MN, a depth measurement of muscle and fat over the loin, were the most accurate predictors of percentage side muscle. In multiple regression, the addition of either eye muscle volume or eye muscle area to hot carcass weight and 10th rib fat thickness gave the most accurate predictions of side muscle weight and percentage side muscle, but in the case of each dependent variable, the improvement in accuracy was slight compared with that of the two most accurate regressors, hot carcass weight and 10th rib fat thickness. Although eye muscle volume was a more accurate predictor of side muscle weight than eye muscle area in simple regression, their contributions in multiple regression with hot carcass weight and 10th rib fat thickness were similar. None of the five linear measurements associated with m. longissimus contributed significantly to improving the prediction of weight or percentage of side muscle.     

Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat)

Cow ghee is very used in some regions of Iran, such as Kermanshah province. Cow ghee is a natural source that contains high-quality nutrients which are needed for the human body. Adulteration in dairy products is not only a serious threat to human health but also it causes economic losses. Diagnosis of foodstuff cheating and its estimation is one of the key concerns in recent years. The aim of this study was the detection of the adulteration in cow ghee by olfactory machine system. Therefore, an electronic nose system was used for the different levels of sunflower oil and cow body fat mixed with pure cow ghee (10%, 20%, 30%, 40%, and 50%). The principal components analysis (PCA) and artificial neural networks (ANNs) methods were used to achieve this goal. Based on the results, the accuracy of the principal components analysis of sunflower oil and cow body fat were 96% and 97% of the data variance, respectively. According to the results, artificial neural networks identified the adulteration with sunflower oil and cow body fat with an accuracy of 91.3% and 82.5%, respectively.     

Partial replacement of corn grain by hydrogenated oil in grazing dairy cows in early lactation

Thirty-six grazing dairy cows were used to determine milk production and composition, and dry matter and energy intake when corn grain was partially replaced by hydrogenated oil in the concentrate. Four additional cows, each fitted with a ruminal cannula, were used in a crossover design to evaluate effects of supplemental fat on rumen environment and pasture digestion. All cows grazed mixed pastures with an herbage allowance of 30 kg dry matter/cow per day. The control group was fed a concentrate containing corn grain (4.49 kg dry matter/cow per day) and fishmeal (0.37 kg dry matter/cow per day), whereas the other group (fat) received a concentrate containing corn grain (2.87 kg dry matter/cow per day), fishmeal (0.37 kg dry matter/cow per day) and fat (0.7 kg dry matter/cow per day). The fat was obtained by hydrogenation of vegetable oils (melting point 58 to 60 degrees C, 30.3% C16:0, 34.9% C18:0, 21.8% C18:1, 3.3% C18:2). Supplemental fat increased milk production (control = 23.7 vs. fat = 25.0 kg/cow per day), fat-corrected milk (control = 22.5 vs. fat = 24.5 kg/cow per day), milk fat content (control = 3.64% vs. fat = 3.86%) and yields of milk fat (control = 0.86 vs. fat = 0.97 kg/cow per day) and protein (control = 0.74 vs. fat = 0.78 kg/cow per day). Milk percentages of protein, lactose, casein, cholesterol, and urea nitrogen were not affected. Pasture DMI and total DMI of pasture and concentrate and estimated energy intake were unchanged. No differences in loss of body weight or body condition score were detected. Plasma concentrations of nonesterified fatty acids, somatotropin, insulin, and insulin-like growth factor were not affected by supplemental fat. Concentrations of plasma triglyceride and total cholesterol were increased by supplemented fat, and no changes in plasma glucose and urea nitrogen were observed. The acetate-to-propionate ratio was higher in rumen fluid of cows that consumed fat (fat = 3.39 vs. control = 3.27). In situ pasture NDF degradation was not affected. The partial replacement of corn grain with fat improved the productive performance of early-lactation cows grazing spring pastures. No negative effects of supplemental fat on ruminal fiber digestion were detected.     

Postpartum body condition score and results from the first test day milk as predictors of disease, fertility, yield, and culling in commercial dairy herds

The study used field data from a regular herd health service to investigate the relationships between body condition scores or first test day milk data and disease incidence, milk yield, fertility, and culling. Path model analysis with adjustment for time at risk was applied to delineate the time sequence of events. Milk fever occurred more often in fat cows, and endometritis occurred between calving and 20 d of lactation more often in thin cows. Fat cows were less likely to conceive at first service than were cows in normal condition. Fat body condition postpartum, higher first test day milk yield, and a fat to protein ratio of > 1.5 increased body condition loss. Fat or thin condition or condition loss was not related to other lactation diseases, fertility parameters, milk yield, or culling. First test day milk yield was 1.3 kg higher after milk fever and was 7.1 kg lower after displaced abomasum. Higher first test day milk yield directly increased the risk of ovarian cyst and lameness, increased 100-d milk yield, and reduced the risk of culling and indirectly decreased reproductive performance. Cows with a fat to protein ratio of > 1.5 had higher risks for ketosis, displaced abomasum, ovarian cyst, lameness, and mastitis. Those cows produced more milk but showed poor reproductive performance. Given this type of herd health data, we concluded that the first test day milk yield and the fat to protein ratio were more reliable indicators of disease, fertility, and milk yield than was body condition score or loss of body condition score.     

The use of ultrasound to predict the carcass composition of live Akkaraman lambs

The aim of this study was to measure fat thickness, area and depth of the longissimus dorsi muscle using ultrasonography, to estimate carcass composition in live Akkaraman lambs. Fat thickness, area and depth of the longissimus dorsi muscle between the 12th and 13th ribs were measured in vivo and on the carcass after slaughter, using real time ultrasound in 40 Akkaraman lambs. To estimate the carcass composition, one-half of a carcass was dissected into muscle, fat and bone after slaughter. Overall, correlation coefficients between ultrasound and carcass longissimus dorsi muscle area, depth and fat thickness were 0.82, 0.60 and 0.77, respectively. Estimates of carcass composition for Akkaraman lambs based on LW explained 78%, 82%, 74%, 52%, 75%, 36% and 72% of the variations for muscle, total carcass fat, subcutaneous fat, inter-muscular fat, non-carcass fat, tail fat and bone, respectively. The introduction of UFT, ULMA and ULMD as independent variables in addition to LW in the multiple linear regression equations further improved the variations for total muscle (80%), carcass fat (84%) and bone weight (76%) whereas no improvement was observed for subcutaneous, intermuscular, non-carcass and tail fat. The results showed that in vivo ultrasound fat thickness and measurement of area and depth of the longissimus dorsi muscle in association with live weight could be used to estimate muscle, total body fat and bone weight in Akkaraman lambs.     

Estimating daily fat yield from a single milking on test day for herds with a robotic milking system

The objective of this study was to estimate the daily fat yield and fat percentage from one sampled milking per cow per test day in an automatic milking system herd, when the milking times and milk yields of all individual milkings are recorded by the automatic milking system. Multiple regression models were used to estimate the 24-h fat percentage when only one milking is sampled for components and milk yields and milking times are known for all milkings in the 24-h period before the sampled milking. In total, 10,697 cow test day records, from 595 herd tests at 91 Dutch herds milked with an automatic milking system, were used. The best model to predict 24-h fat percentage included fat percentage, protein percentage, milk yield and milking interval of the sampled milking, milk yield, and milking interval of the preceding milking, and the interaction between milking interval and the ratio of fat and protein percentage of the sampled milking. This model gave a standard deviation of the prediction error (SE) for 24-h fat percentage of 0.321 and a correlation between the predicted and actual 24-h fat percentage of 0.910. For the 24-h fat yield, we found SE = 90 g and correlation = 0.967. This precision is slightly better than that of present a.m.-p.m. testing schemes. Extra attention must be paid to correctly matching the sample jars and the milkings. Furthermore, milkings with an interval of less than 4 h must be excluded from sampling as well as milkings that are interrupted or that follow an interrupted milking. Under these restrictions (correct matching, interval of at least 4 h, and no interrupted milking), one sampled milking suffices to get a satisfactory estimate for the test-day fat yield.     

Prediction of suckling lamb carcass composition from objective and subjective carcass measurements

Correlations and regression between fatness and carcass conformation measurements and carcass tissue composition has been studied for both sexes in suckling lambs, with slaughter weights between 9 and 15 kg. Objective fat measurements showed higher correlations than subjective fat measurements, mainly with carcass fat. Conformation measurements presented greater correlations with tissues weight than with tissues proportion (r0.50). CCW was highly correlated with total muscle in the carcass (R²=0.961). Two fatness measurements (KKCF proportion and dorsal fat thickness) were included for the prediction of the muscle proportion. Furthermore, the model included other measurements, such as fore cannon bone weight and internal carcass length although they displayed very low correlation coefficients. This equation explained 64.1% of the variation of carcass muscle proportion (r.s.d.=1.532). CCW was included in the prediction equations of tissue weights, although in the fat tissue equation it appeared in the form of the carcass compactness index (CCW/L). CCW was a good predictor of the weight of carcass tissues in suckling lambs, especially for muscle. Prediction equations for tissue composition in grams were more accurate (R²0.91) than those for tissue proportion. When gender was included in all models it did not improve either accuracy or precision of the prediction.     

共轭亚油酸钙营养性改善机体肌肉组成的实验研究

共轭亚油酸(CLA)是一种新型的保健脂肪酸,共轭亚油酸钙(CLA-Ca)是由CLA经合成得到的活性化合物。研究考察了CLA-Ca营养性改善机体肌肉组成的生物有效性。用CLA-Ca饲喂育肥猪,按GB8467-1987测定。结果表明,CLA-Ca饲喂64 d后,育肥猪平均膘厚和B超膘厚下降12.16%与29.51%,胴体眼肌面积和B超眼肌面积提高6.83%与5.92%,瘦肉率高于对照组6.71%。实验证实,CLA-Ca可有效地改善机体肌肉组成,促进营养物质再分配,显著提高育肥猪的胴体瘦肉率。     

Carcass classification in suckling lambs. Discrimination ability of the European Union scale

Forty-nine Manchega suckling lambs were used in this trial. Lambs were slaughtered at 10, 12 and 14 kg liveweight (cold carcass weight lower than 13 kg). The degree of fatness was assessed by three assessors on colour photographs of the carcasses, using the European Union scale for light lambs (EU), and another new scale developed in our laboratory (Suckling Lamb scale, SL). Carcass degree of fatness was reassessed using the same scales divided into 0.25 point-intervals (EUI, SLI). Carcasses were allocated into four groups by means of a tree classification algorithm (cluster analysis), according to 27 variates, namely muscle percentage, whole fat percentage, subcutaneous fat, intermuscular fat, kidney knob and channel fat percentage and muscle percentage, both in the carcass and in three joints (leg, best end neck plus loin, and shoulder). SL scale groups showed a higher number of variates well correlated to assessors' scoring than EU scale groups. Nevertheless, the EU scale showed significant between-groups differences in a higher number of variates (P<0.01). Using 0.25-points interval scales, EUI scale scoring was the best correlated to tissue composition. However, most variates were better correlated to dorsal fat thickness measurement (4 cm back from last rib and 4 cm from the carcass midline) than to assessors' scoring. All four groups in the tree classification were statistically different from one another, but assessors only were able to detect differences between whole fat percentage in group 1 and the rest. These results suggest that it is possible to improve the photographic standards of the EU method by using a 0.25 points interval scale. In abattoir conditions, the new SL method (1.0 point intervals) proved to be better. Measure of dorsal fat thickness proved to be a very good predictor of carcass tissue composition, but it is a more expensive measure.