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A regular and repeated recording of body weight (BW) is useful information for herd management. BW can be predicted regularly from animal characteristics such as age, lactation number, or lactation stage. Those traits are unfortunately animal unspecific. Adding animal specific information, which can be easily obtained on a large scale, to the BW prediction would be of utmost importance. There are good scientific reasons to suspect links between BW and animal specific characteristics, available in a repeated fashion, such as milk yield and milk composition. This study aimed to demonstrate the feasibility of predicting test-day BW from stage, lactation number, month of test, milk yield and mid-infrared spectra, representing milk composition. Five models were tested initially from 721 BW records collected in 6 herds: day in milk + number of lactation (equation 1a); equation1a + milk yield (equation 1b); only spectral data (equation 1c); equation 1c + equation 1a (equation 2); equation 2 + milk yield (equation 3). Then 3 other equations included the same explicative variables, except that the spectral data were regressed using second order Legendre Polynomials (PL) to take into account changes of spectral data within lactation. Equation 1a and 1b were built using linear regressions and equation 1c until 3 were built using partial least square regressions. These 3 last equations had a higher number of factors. Adding of MIR data in the equation increased of 7% the values of cross-validation R² (R²cv). Potential BW outliers were discarded using a residual analysis based on equation 3. From 662 records, the following statistical parameters were obtained: the calibration coefficient of determination (R²c) = 0.65, R²cv = 0.61, calibration root mean squared error of prediction (RMSEP)=38 kg, and RMSEPcv=40 kg. Low variation of R²c and RMSEPc values obtained from the herd validation confirmed the herd independence of predictions. However, large variability was observed for RMSEPv (37 to 64 kg) suggesting the need to increase the dataset in order to improve the robustness of the equation. By applying the equations on a large spectral database, it was confirmed that the addition of MIR data allows to better model the BW evolution within lactation. Based on these preliminary results, and if a larger validation confirms these findings, this approach could be used to develop equations that are better able to assess BW throughout lactation(s), BW being an important element for management and selection tools.

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P

The calving interval (CI) can potentially impact the economic results of dairy farms. This study highlighted the most profitable CI and innovated by describing this optimum as a function of the feeding system of the farm. On-farm data were used to represent real farm conditions. A total of 1832 accounts of farms recorded from 2007 to 2014 provided economic, technical and feeding information per herd and per year. A multiple correspondence analysis created four feeding groups: extensive, low intensive, intensive and very intensive herds. The gross margin and some of its components were corrected to account for the effect of factors external to the farm, such as the market, biological status, etc. Then the corrected gross margin (cGMc) and its components were modelled by CI parameters in each feeding system by use of GLM. The relationship between cGMc and the proportion of cows with CI

The segregation of the causal mutation () in the muscular hypertrophy gene in dual-purpose Belgian Blue (dpBB) cattle is considered to result in greater calving difficulty (dystocia). Establishing adapted genetic evaluations might overcome this situation through efficient selection. However, the heterogeneity of dpBB populations at the locus implies separating the major gene and other polygenic effects in complex modeling. The use of mixed inheritance models may be an interesting option because they simultaneously assume both influences. A genetic evaluation in dpBB based on a mixed inheritance model was developed for birth and conformation traits: gestation length (GL), calving difficulty (CD), birth weight (BiW), and body conformation score (BC). A total of 27,362 animals having records were used for analyses. The total number of animals in the pedigree used to build the numerator relationship matrix was 62,617. Genotypes at the locus were available for 2,671 animals. Missing records at this locus were replaced with genotype probabilities. A total of 13,221 (48.3%) were registered as dpBB, 1,287 (4.7%) as beef Belgian Blue, and 12,854 (47.0%) were unknown. From those 13,221 dpBB animals, 650, 849, and 534 had double or single copies or no copy, respectively, of the causal mutation () in the muscular hypertrophy gene, whereas 11,188 had missing genotypes. This heterogeneity at the locus may be the reason for high variability in the studied traits, that is, high heritability estimates of 0.33, 0.30, 0.38, and 0.43 for GL, CD, BiW, and BC, respectively. In general, additive ( < 0.05) and dominance ( < 0.001) allele substitution for calves and dams had significant impact for all traits. The moderate coefficient of genetic variation (27.80%) and high direct heritability (0.28) for CD suggested genetic variability in dpBB and possible genetic improvement through selection. This variability has allowed dpBB breeders to successfully apply mass selection in the past. Genetic trend means from 1988 to 2016 showed that sire selection for CD within genotype was progressively applied by breeders. The selection intensity was more important for CD in double-muscled lines than in segregated lines. Our study illustrated the possible confusion caused by the use of major genes in selection and the importance of fitting appropriate models such as mixed inheritance models that combine polygenic and gene content information.

Recent publications indicate that single-step models are suitable to estimate breeding values, dominance deviations and total genetic values with acceptable quality. Additive single-step methods implicitly extend known number of allele information from genotyped to non-genotyped animals. This theory is well derived in an additive setting. It was recently shown, at least empirically, that this basic strategy can be extended to dominance with reasonable prediction quality. Our study addressed two additional issues. It illustrated the theoretical basis for extension and validated genomic predictions to dominance based on single-step genomic best linear unbiased prediction theory. This development was then extended to include inbreeding into dominance relationships, which is a currently not yet solved issue. Different parametrizations of dominance relationship matrices were proposed. Five dominance single-step inverse matrices were tested and described as C1 , C2 , C3 , C4 and C5 . Genotypes were simulated for a real pig population (n = 11,943 animals). In order to avoid any confounding issues with additive effects, pseudo-records including only dominance deviations and residuals were simulated. SNP effects of heterozygous genotypes were summed up to generate true dominance deviations. We added random noise to those values and used them as phenotypes. Accuracy was defined as correlation between true and predicted dominance deviations. We conducted five replicates and estimated accuracies in three sets: between all (S1 ), non-genotyped (S2 ) and inbred non-genotyped (S3 ) animals. Potential bias was assessed by regressing true dominance deviations on predicted values. Matrices accounting for inbreeding (C3 , C4 and C5 ) best fit. Accuracies were on average 0.77, 0.40 and 0.46 in S1 , S2 and S3 , respectively. In addition, C3 , C4 and C5 scenarios have shown better accuracies than C1 and C2 , and dominance deviations were less biased. Better matrix compatibility (accuracy and bias) was observed by re-scaling diagonal elements to 1 minus the inbreeding coefficient (C5 ).

Simple Summary There is a growing interest in using milk mid-infrared (MIR) spectrometry to obtain new phenotypes to assist in the complex management of dairy farms. These predictive values can be erroneous for many reasons, even if the prediction equations used are accurate. Unfortunately, there is no quality protocol routinely implemented to detect those abnormal predictive values in the database recorded by dairy herd improvement (DHI) organizations, except for fat and protein contents. However, for financial and practical reasons, it is unfeasible to adapt the quality protocol commonly used in milk laboratories to improve the accuracy of those traits. So, this study proposes three different statistical methods that would be easy to implement by DHI organizations to detect abnormal values and limit the spectral extrapolation in order to improve the accuracy of MIR-based predictive values. Abstract The use of abnormal milk mid-infrared (MIR) spectrum strongly affects prediction quality, even if the prediction equations used are accurate. So, this record must be detected after or before the prediction process to avoid erroneous spectral extrapolation or the use of poor-quality spectral data by dairy herd improvement (DHI) organizations. For financial or practical reasons, adapting the quality protocol currently used to improve the accuracy of fat and protein contents is unfeasible. This study proposed three different statistical methods that would be easy to implement by DHI organizations to solve this issue: the deletion of 1% of the extreme high and low predictive values (M1), the deletion of records based on the Global-H (GH) distance (M2), and the deletion of records based on the absolute fat residual value (M3). Additionally, the combinations of these three methods were investigated. A total of 346,818 milk samples were analyzed by MIR spectrometry to predict the contents of fat, protein, and fatty acids. Then, the same traits were also predicted externally using their corresponded standardized MIR spectra. The interest in cleaning procedures was assessed by estimating the root mean square differences (RMSDs) between those internal and external predicted phenotypes. All methods allowed for a decrease in the RMSD, with a gain ranging from 0.32% to 41.39%. Based on the obtained results, the “M1 and M2” combination should be preferred to be more parsimonious in the data loss, as it had the higher ratio of RMSD gain to data loss. This method deleted the records based on the 2% extreme predictions and a GH threshold set at 5. However, to ensure the lowest RMSD, the “M2 or M3” combination, considering a GH threshold of 5 and an absolute fat residual difference set at 0.30 g/dL of milk, was the most relevant. Both combinations involved M2 confirming the high interest of calculating the GH distance for all samples to predict. However, if it is impossible to estimate the GH distance due to a lack of relevant information to compute this statistical parameter, the obtained results recommended the use of M1 combined with M3. The limitation used in M3 must be adapted by the DHI, as this will depend on the spectral data and the equation used. The methodology proposed in this study can be generalized for other MIR-based phenotypes.

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.

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (

Changes in milk production traits (i.e., milk yield, fat, and protein contents) with the pregnancy stage are well documented. To our knowledge, the effect of pregnancy on the detailed milk composition has not been studied so far. The mid-infrared (MIR) spectrum reflects the detailed composition of a milk sample and is obtained by a nonexhaustive and widely used method for milk analysis. Therefore, this study aimed to investigate the effect of pregnancy on milk MIR spectrum in addition to milk production traits (milk yield, fat, and protein contents). A model including regression on the number of days pregnant was applied on milk production traits (milk yield, fat, and protein contents) and on 212 spectral points from the MIR spectra of 9,757 primiparous Holstein cows from Walloon herds. Effects of pregnancy stage were expressed on a relative scale (effect divided by the squared root of the phenotypic variance); this allowed comparisons between effects on milk traits and on 212 spectral points. Effect of pregnancy stage on production traits were in line with previous studies indicating that the model accounted well for the pregnancy effect. Trends of the relative effect of the pregnancy stage on the 212 spectral points were consistent with known and observed effect on milk traits. The highest effect of the pregnancy was observed in the MIR spectral region from 968 to 1,577 cm−1. For some specific wavenumbers, the effect was higher than for fat and protein contents in the beginning of the pregnancy (from 30 to 90 or 120 d pregnant). In conclusion, the effect of early pregnancy can be observed in the detailed milk composition through the analysis of the MIR spectrum of bovine milk. Further analyses are warranted to explore deeply the use of MIR spectra of bovine milk for breeding and management of dairy cow pregnancy.

peer-reviewed Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n = 110), Ireland (n = 1658) and Scotland (n = 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS × LTF effects were significant (P < 0.001 and P = 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r = 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin – Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events – external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality. European Commission, Directorate-General for Agriculture and Rural Development, under Grant Agreement 211708; Commission of the European Communities, FP7, KBBE-2007-1; National Fund for Scientific Research (Brussels, Belgium)grant FRFC 2.4604.11; Ministry of Agriculture of the Walloon Region of Belgium (project NovaUdderHealth D31-1207).

To manage negative energy balance and ketosis in dairy farms, rapid and cost-effective detection is needed. Among the milk biomarkers that could be useful for this purpose, acetone and β-hydroxybutyrate (BHB) have been proved as molecules of interest regarding ketosis and citrate was recently identified as an early indicator of negative energy balance. Because Fourier transform mid-infrared spectrometry can provide rapid and cost-effective predictions of milk composition, the objective of this study was to evaluate the ability of this technology to predict these biomarkers in milk. Milk samples were collected in commercial and experimental farms in Luxembourg, France, and Germany. Acetone, BHB, and citrate contents were determined by flow injection analysis. Milk mid-infrared spectra were recorded and standardized for all samples. After edits, a total of 548 samples were used in the calibration and validation data sets for acetone, 558 for BHB, and 506 for citrate. Acetone content ranged from 0.020 to 3.355mmol/L with an average of 0.103mmol/L; BHB content ranged from 0.045 to 1.596mmol/L with an average of 0.215mmol/L; and citrate content ranged from 3.88 to 16.12mmol/L with an average of 9.04mmol/L. Acetone and BHB contents were log-transformed and a part of the samples with low values was randomly excluded to approach a normal distribution. The 3 edited data sets were then randomly divided into a calibration data set (3/4 of the samples) and a validation data set (1/4 of the samples). Prediction equations were developed using partial least square regression. The coefficient of determination (R(2)) of cross-validation was 0.73 for acetone, 0.71 for BHB, and 0.90 for citrate with root mean square error of 0.248, 0.109, and 0.70mmol/L, respectively. Finally, the external validation was performed and R(2) obtained were 0.67 for acetone, 0.63 for BHB, and 0.86 for citrate, with respective root mean square error of validation of 0.196, 0.083, and 0.76mmol/L. Although the practical usefulness of the equations developed should be further verified with other field data, results from this study demonstrated the potential of Fourier transform mid-infrared spectrometry to predict citrate content with good accuracy and to supply indicative contents of BHB and acetone in milk, thereby providing rapid and cost-effective tools to manage ketosis and negative energy balance in dairy farms.

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest.

Based on a Bayesian view of linear mixed models, several studies showed the possibilities to integrate estimated breeding values (EBV) and associated reliabilities (REL) provided by genetic evaluations performed outside a given evaluation system into this genetic evaluation. Hereafter, the term "internal" refers to this given genetic evaluation system, and the term "external" refers to all other genetic evaluations performed outside the internal evaluation system. Bayesian approaches integrate external information (i.e., external EBV and associated REL) by altering both the mean and (co)variance of the prior distributions of the additive genetic effects based on the knowledge of this external information. Extensions of the Bayesian approaches to multivariate settings are interesting because external information expressed on other scales, measurement units, or trait definitions, or associated with different heritabilities and genetic parameters than the internal traits, could be integrated into a multivariate genetic evaluation without the need to convert external information to the internal traits. Therefore, the aim of this study was to test the integration of external EBV and associated REL, expressed on a 305-d basis and genetically correlated with a trait of interest, into a multivariate genetic evaluation using a random regression test-day model for the trait of interest. The approach we used was a multivariate Bayesian approach. Results showed that the integration of external information led to a genetic evaluation for the trait of interest for, at least, animals associated with external information, as accurate as a bivariate evaluation including all available phenotypic information. In conclusion, the multivariate Bayesian approaches have the potential to integrate external information correlated with the internal phenotypic traits, and potentially to the different random regressions, into a multivariate genetic evaluation. This allows the use of different scales, heritabilities, variance components, measurement units, or trait definitions for external and internal traits. However, one possible issue for implementing multivariate Bayesian approaches could be the availability or estimation of genetic correlations between external and internal traits.

Background A condition to predict unbiased estimated breeding values by best linear unbiased prediction is to use simultaneously all available data. However, this condition is not often fully met. For example, in dairy cattle, internal (i.e. local) populations lead to evaluations based only on internal records while widely used foreign sires have been selected using internally unavailable external records. In such cases, internal genetic evaluations may be less accurate and biased. Because external records are unavailable, methods were developed to combine external information that summarizes these records, i.e. external estimated breeding values and associated reliabilities, with internal records to improve accuracy of internal genetic evaluations. Two issues of these methods concern double-counting of contributions due to relationships and due to records. These issues could be worse if external information came from several evaluations, at least partially based on the same records, and combined into a single internal evaluation. Based on a Bayesian approach, the aim of this research was to develop a unified method to integrate and blend simultaneously several sources of information into an internal genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. Results This research resulted in equations that integrate and blend simultaneously several sources of information and avoid double-counting of contributions due to relationships and due to records. The performance of the developed equations was evaluated using simulated and real datasets. The results showed that the developed equations integrated and blended several sources of information well into a genetic evaluation. The developed equations also avoided double-counting of contributions due to relationships and due to records. Furthermore, because all available external sources of information were correctly propagated, relatives of external animals benefited from the integrated information and, therefore, more reliable estimated breeding values were obtained. Conclusions The proposed unified method integrated and blended several sources of information well into a genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. The unified method can also be extended to other types of situations such as single-step genomic or multi-trait evaluations, combining information across different traits. Electronic supplementary material The online version of this article (doi:10.1186/s12711-014-0059-3) contains supplementary material, which is available to authorized users.

SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is a tool widely used to help in decision making in complex systems. It suits to exploring the issues and measures related to the conservation and development of local breeds, as it allows the integration of many driving factors influencing breed dynamics. We developed a quantified SWOT method as a decision-making tool for identification and ranking of conservation and development strategies of local breeds, and applied it to a set of 13 cattle breeds of six European countries. The method has four steps definition of the system, identification and grouping of the driving factors, quantification of the importance of driving factors and identification and prioritization of the strategies. The factors were determined following a multi-stakeholder approach and grouped with a three-level structure. Animal genetic resources expert groups ranked the factors, and a quantification process was implemented to identify and prioritize strategies. The proposed SWOT methodology allows analyzing the dynamics of local cattle breeds in a structured and systematic way. It is a flexible tool developed to assist different stakeholders in defining the strategies and actions. The quantification process allows the comparison of the driving factors and the prioritization of the strategies for the conservation and development of local cattle breeds. We identified 99 factors across the breeds. Although the situation is very heterogeneous, the future of these breeds may be promising. The most important strengths and weaknesses were related to production systems and farmers. The most important opportunities were found in marketing new products, whereas the most relevant threats were found in selling the current products. The across-breed strategies utility decreased as they gained specificity. Therefore, the strategies at European level should focus on general aspects and be flexible enough to be adapted to the country and breed specificities. Copyright © The Animal Consortium 2012.

Our aim was to identify elements useful in designing policies and programmes for conservation of farm animal genetic resources, taking as case study a group of European local cattle breeds. We first investigated the implications of differences among countries in the policies and programmes to be developed. Secondly, we analysed key elements common to countries, which may affect local breed viability. We used the herd size trend expected by the farmer in the near future as an indicator of breed viability. Fifteen breeds, for a total of 355 farms, were surveyed. To take into account the multiple factors influencing breeds’ demographic trends, the questionnaire included economical, technical and social aspects. Among the major differences across countries was the perception of the farmer on the value attributed to the local breed by society. Concerning the elements common to countries and their association to breed viability, the greater the collaboration among farmers and the stakeholders’ appreciation as perceived by the farmer, the greater the viability of the farm. An opposite trend was observed for the age of the farmer. Older farmers generally planned to soon cease farming or decrease herd size, whereas young farmers planned to increase the size of their herds. Implications of including these elements in conservation polices are discussed

Bovine lactoferrin (LF) is mainly present in milk and shows important physiological and biological functions. The aim of this study was to estimate the heritability and correlation values of LF content in bovine milk with different economic traits as milk yield (MY), fat and protein percentages, and somatic cell score (SCS). Variance components of the studied traits were estimated by REML using a multiple-trait mixed model. The obtained heritability (0.22) for LF content predicted using mid-infrared spectrometry (pLF) suggested the possibility of animal selection based on the increase of LF content in milk. The phenotypic and genetic correlation values calculated between pLF and SCS were moderate (0.31 and 0.24, respectively). Furthermore, a preliminary study of bovine LF gene polymorphism effects was performed on the same production traits. By PCR, all exons of the LF gene were amplified and then sequenced. Three new polymorphisms were detected in exon 2, exon 11, and intron 8. We examined the effects of LF gene polymorphisms of exons 2, 4, 9, 11, and 15, and intron 8 on pLF, MY, fat and protein percentages, and SCS. The different observed effects did not reach a significant level probably because of the characteristics of the studied population. However, the results were promising, and LF may be a potential indicator of mastitis. Further studies are necessary to evaluate the effect of genetic selection based on LF content on the improvement of mastitis resistance.

International audience; The growth hormone secretagogue receptor (GHSR) is involved in the regulation of energetic homeostasis and GH secretion. In this study, the bovine GHSR gene was mapped to BTA1 between BL26 and BMS4004. Two different bovine GHSR CDS (GHSR1a and GHSR1b) were sequenced. Six polymorphisms (five SNPs and one 3-bp indel) were also identified, three of them leading to amino acid variations L24V, D194N, and Del R242. These variations are located in the extracellular N-terminal end, the exoloop 2, and the cytoloop 3 of the receptor, respectively.

The effects of lactoferrin (LF) on the immune system have already been shown by many studies. Unfortunately, the current methods used to measure LF levels in milk do not permit the study of the genetic variability of lactoferrin or the performance of routine genetic evaluations. The first aim of this research was to derive a calibration equation permitting the prediction of LF in milk by mid-infrared spectrometry (MIR). The calibration with partial least squares on 69 samples showed a ratio of standard error of cross-validation to standard deviation equal to 1.98. Based on this value, the calibration equation was used to establish an LF indicator trait (predicted LF; pLF) on a large number of milk samples (n = 7,690). A subsequent study of its variability was conducted, which confirmed that stage of lactation and lactation number influence the overall pLF level. Small differences in mean pLF among 7 dairy breeds were also observed. The pLF content of Jersey milk was significantly higher than that in Holstein milk. Therefore, the choice of breed could change the expected LF level. Heritability estimated for pLF was 19.7%. The genetic and phenotypic correlations between somatic cell score and pLF were 0.04 and 0.26, respectively. As somatic cell score increases in presence of mastitis, this observation seems to indicate that pLF, or a function of observed pLF, compared with expected LF might have potential as an indicator of mastitis. The negative genetic correlation (-0.36) between milk yield and pLF could indicate an undesirable effect of selection for high milk production on the overall LF level.

An increasing number of models are being developed to provide information from milk Fourier transform mid-infrared (FT-MIR) spectra on fine milk composition, technological properties of milk, or even cows' physiological status. In this context, and to take advantage of these existing models, the purpose of this work was to evaluate whether a spectral standardization method can enable the use of multiple equations within a network of different FT-MIR spectrometers. The piecewise direct standardization method was used, matching "slave" instruments to a common reference, the "master." The effect of standardization on network reproducibility was assessed on 66 instruments from 3 different brands by comparing the spectral variability of the slaves and the master with and without standardization. With standardization, the global Mahalanobis distance from the slave spectra to the master spectra was reduced on average from 2,655.9 to 14.3, representing a significant reduction of noninformative spectral variability. The transfer of models from instrument to instrument was tested using 3 FT-MIR models predicting (1) the quantity of daily methane emitted by dairy cows, (2) the concentration of polyunsaturated fatty acids in milk, and (3) the fresh cheese yield. The differences, in terms of root mean squared error, between master predictions and slave predictions were reduced after standardization on average from 103 to 17 g/d, from 0.0315 to 0.0045 g/100 mL of milk, and from 2.55 to 0.49 g of curd/100 g of milk, respectively. For all the models, standard deviations of predictions among all the instruments were also reduced by 5.11 times for methane, 5.01 times for polyunsaturated fatty acids, and 7.05 times for fresh cheese yield, showing an improvement of prediction reproducibility within the network. Regarding the results obtained, spectral standardization allows the transfer and use of multiple models on all instruments as well as the improvement of spectral and prediction reproducibility within the network. The method makes the models universal, thereby offering opportunities for data exchange and the creation and use of common robust models at an international level to provide more information to the dairy sector from direct analysis of milk.