84 research outputs found
The use of mid-infrared spectrometry to predict body energy status of Holstein cows
Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application
Indices for simultaneously evaluation of hydrological drought
The aim of this paper is to identify differences in genetic variation of fatty acid (FA) composition in milk in different breeds. Data used included Meuse-Rhine-Yssel (MRY) and Holstein Friesian (HF) cattle breeds which were raised in the Netherlands. Both populations participated in the same milk recording system, but differed in selection history, where in the MRY there has been relatively very little emphasis on selection for high-input high-output production systems compared to HF. Differences in genetic variation were investigated by estimating breed specific additive genetic variances and heritabilities for FA contents in milk of MRY and HF. Mid Infrared Spectrometry spectra were used to predict total fat percentage and detailed FA contents in milk (14 individual FA and 14 groups of FA in g of fat/dL of milk). The dataset for MRY contained 2916 records from 2049 registered cows having at least 50% genes of MRY origin and the dataset used for HF contained 155,319 records from 96,315 registered cows having at least 50% genes of HF origin. Variance components of individual FA content in milk for the different breeds were estimated using a single trait animal model. Additive genetic variances for FA produced through de novo synthesis (short chain FA, C12:0, C14:0, and partly C16:0), C14:1 c-9 and C16:1 c-9 were significantly higher (
Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers
Evaluation and mitigation of enteric methane (CH4) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH4 reference measurements obtained with the SF6 tracer technique to determine ruminal CH4 production, a current equation permits evaluation of individual daily CH4 emissions of dairy cows based on milk Fourier transform mid-infrared (FT-MIR) spectra. However, the respiration chamber (RC) technique is considered to be more accurate than SF6 to measure CH4 production from cattle. This study aimed to develop an equation that allows estimating CH4 emissions of lactating cows recorded in an RC from corresponding milk FT-MIR spectra and to challenge its robustness and relevance through validation processes and its application on a milk spectral database. This would permit confirming the conclusions drawn with the existing equation based on SF6 reference measurements regarding the potential to estimate daily CH4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH4 measurements (mean ± standard deviation of 400 ± 72 g of CH4/d) and corresponding standardized milk mid-infrared spectra were obtained from 148 individual lactating cows between 7 and 321 d in milk in 5 European countries (Germany, Switzerland, Denmark, France, and Northern Ireland). The developed equation based on RC measurements showed calibration and cross-validation coefficients of determination of 0.65 and 0.57, respectively, which is lower than those obtained earlier by the equation based on 532 SF6 measurements (0.74 and 0.70, respectively). This means that the RC-based model is unable to explain the variability observed in the corresponding reference data as well as the SF6-based model. The standard errors of calibration and cross-validation were lower for the RC model (43 and 47 g/d vs. 66 and 70 g/d for the SF6 version, respectively), indicating that the model based on RC data was closer to actual values. The root mean squared error (RMSE) of calibration of 42 g/d represents only 10% of the overall daily CH4 production, which is 23 g/d lower than the RMSE for the SF6-based equation. During the external validation step an RMSE of 62 g/d was observed. When the RC equation was applied to a standardized spectral database of milk recordings collected in the Walloon region of Belgium between January 2012 and December 2017 (1,515,137 spectra from 132,658 lactating cows in 1,176 different herds), an average ± standard deviation of 446 ± 51 g of CH4/d was estimated, which is consistent with the range of the values measured using both RC and SF6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH4 emissions from dairy cows provided that the variability to predict is covered by the model
A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra
peer-reviewedLactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set
Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes
The effects of cow genetic group on the density of raw whole milk
peer reviewedThe density of milk is dependent upon various factors including temperature, processing conditions, and
animal breed. This study evaluated the effect of different cow genetic groups, Jersey, elite Holstein Friesians
(EHF), and national average Holstein Friesians (NAHF) on the compositional and physicochemical properties
of milk. Approximately 1,040 representative (morning and evening) milk samples (~115 per month during
9 mo) were collected once every 2 wk. Milk composition was determined with a Bentley Dairyspec instrument.
Data were analysed with a mixed linear model that included the fixed effects of sampling month, genetic
group, interaction between month and genetic group and the random effects of cow to account for repeated
measures on the same animal. Milk density was determined using three different analytical approaches –
a portable and a standard desktop density meter and 100 cm3 calibrated glass pycnometers. Milk density was
analysed with the same mixed model as for milk composition but including the analytical method as a fixed effect.
Jersey cows had the greatest mean for fat content (5.69 ± 0.13%), followed by EHF (4.81 ± 0.16%) and NAHF (4.30
± 0.15%). Milk density was significantly higher (1.0313 g/cm³ ± 0.00026, P < 0.05) for the milk of Jersey breed when
compared to the EHF (1.0304 ± 0.00026 g/cm³) and NAHF (1.0303 ± 0.00024 g/cm³) genetic groups. The results from
this study can be used by farmers and dairy processors alike to enhance accuracy when calculating the quantity
and value of milk solids depending upon the genetic merit of the animal/herd, and may also improve milk payment
systems through relating milk solids content and density
Milk coagulation properties and methods of detection
ABSTRACT: One of the most crucial steps in cheesemaking is the coagulation process, and knowledge of the parameters involved in the clotting process plays an important technological role in the dairy industry. Milk of different ruminant species vary in terms of their coagulation capacities because they are influenced by the milk composition and mainly by the milk protein genetic variants. The milk coagulation capacity can be measured by means of mechanical and/or optical devices, such as Lactodynamographic Analysis and Near-Infrared and Mid-Infrared Spectroscopy
Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions
Publication history: Accepted - 7 December 2016; Published online - 1 February 2017.Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting
cows require accurate and large-scale measurements of
methane (CH4) emissions from individual cows. Several
techniques have been developed to measure CH4 in a research setting but most are not suitable for large-scale
recording on farm. Several groups have explored proxies (i.e., indicators or indirect traits) for CH4; ideally
these should be accurate, inexpensive, and amenable
to being recorded individually on a large scale. This
review (1) systematically describes the biological basis
of current potential CH4 proxies for dairy cattle; (2)
assesses the accuracy and predictive power of single
proxies and determines the added value of combining
proxies; (3) provides a critical evaluation of the relative
merit of the main proxies in terms of their simplicity,
cost, accuracy, invasiveness, and throughput; and (4)
discusses their suitability as selection traits. The proxies range from simple and low-cost measurements such
as body weight and high-throughput milk mid-infrared
spectroscopy (MIR) to more challenging measures such
as rumen morphology, rumen metabolites, or microbiome profiling. Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4,
and are costly and difficult to measure routinely onfarm. Proxies related to body weight or milk yield and
composition, on the other hand, are relatively simple,
inexpensive, and high throughput, and are easier to
implement in practice. In particular, milk MIR, along
with covariates such as lactation stage, are a promising
option for prediction of CH4 emission in dairy cows.
No single proxy was found to accurately predict CH4,
and combinations of 2 or more proxies are likely to be
a better solution. Combining proxies can increase the
accuracy of predictions by 15 to 35%, mainly because
different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings
in the other(s). The most important applications of
CH4 proxies are in dairy cattle management and breeding for lower environmental impact. When breeding for
traits of lower environmental impact, single or multiple
proxies can be used as indirect criteria for the breeding
objective, but care should be taken to avoid unfavorable correlated responses. Finally, although combinations of proxies appear to provide the most accurate
estimates of CH4, the greatest limitation today is the
lack of robustness in their general applicability. Future
efforts should therefore be directed toward developing
combinations of proxies that are robust and applicable
across diverse production systems and environments.Technical and financial support from the COST Action FA1302 of the European Union
Short communication: Influence of the muscle hypertrophy mutation of the myostatin gene on milk production traits and milk fatty acid composition in dual-purpose Belgian Blue dairy cattle
Genetic variability of fatty acids in bovine milk
Fatty acids composition of bovine milk influences the technological properties of butterfat and also presents some potential benefits for human health. Impact of feeding on fat composition is well described in the literature; less information is available about the impact of genetics. Based on few studies, essentially conducted to isolate some feeding effect, the breed seemed to influence the fatty acids composition. The variation in the activity of δ-9 desaturase, key enzyme in the production of monounsaturated fatty acids and conjugated linoleic acids in milk, could explain these differences. Very few studies have been focussing on the estimation of genetic parameters of fatty acids composition. However, the moderate heritability estimates observed by these studies for the major fatty acids could suggest a potential genetic effect
- …