Development and validation of a classification model for boar taint detection in pork fat samples

This study aims to characterize a complete volatile organic compound profile of pork neck fat for boar taint prediction. The objectives are to identify specific compounds related to boar taint and to develop a classification model. In addition to the well-known androstenone, skatole and indole, 10 other features were found to be discriminant according to untargeted volatolomic analyses were conducted on 129 samples using HS-SPME-GC×GC-TOFMS. To select the odor-positive samples among the 129 analyzed, the selection was made by combining human nose evaluations with the skatole and androstenone concentrations determined using UHPLC-MS/MS. A comparison of the data of the two populations was performed and a statistical model analysis was built on 70 samples out of the total of 129 samples fully positive or fully negative through these two orthogonal methods for tainted prediction. Then, the model was applied to the 59 remaining samples. Finally, 7 samples were classified as tainted.

Single-step genome-wide association analyses for milk urea concentration in Walloon Holstein cows

The aims of this study were to conduct a single-step genome-wide association to identify genomic regions associated with milk urea (MU) and to estimate genetic correlations between MU and milk yield (MY), milk composition (calcium content [CC], fat percentage [FP], protein percentage [PP], and casein percentage [CNP]), and cheese-making properties (CMP; coagulation time [CT], curd firmness after 30 min from rennet addition [a30], and titratable acidity [TA]). The used data have been collected from 2015 to 2020 on 78,073 first-parity (485,218 test-day records) and 48,766 second-parity (284,942 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 SNP located on 29 BTA of 6,617 animals (1,712 males) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 kb) was calculated, and the top-3 genomic regions explaining the largest rate of the genetic variance were considered promising regions and used to identify potential candidate genes. Mean (SD) MU was 25.38 (8.02) mg/dL and 25.03 (8.06) mg/dL in the first and second lactation, respectively. Mean heritability estimates for daily MU were 0.21 and 0.23 for the first and second lactation, respectively. The genetic correlations estimated between MU and MY, milk composition, and CMP were quite low (ranged from -0.10 [CC] to 0.10 [TA] and -0.05 [CT] to 0.13 [TA] for the first and second lactations, respectively). The top-3 regions associated with MU were located from 80.61 to 80.74 Mb on BTA6, 103.26 to 103.41 Mb on BTA11, and 1.59 to 2.15 Mb on BTA14. Genes including KCNT1, MROH1, SHARPIN, TSSK5, CPSF1, HSF1, TONSL, DGAT1, SCX, and MAF1 were identified as positional candidate genes for MU. The findings of this study can be used for a better understanding of the genomic architecture underlying MU in Holstein cattle.

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

Estimation of genetic parameters and single-step genome-wide association studies for eating time and rumination time in Holstein dairy cows

The aims of this study were to estimate genetic parameters and to identify genomic regions associated with eating time (ET) and rumination time (RUT) in Holstein dairy cows. Genetic correlations among ET, RUT, and milk yield traits were also estimated. The data were collected from 2019 to 2022 in 6 dairy herds located in the Walloon Region of Belgium. The dataset consisted of daily ET and RUT records on 284 Holstein cows, from which 41 cows had records only for the first parity (P1), 101 cows had records from both the first and second parities, and 142 cows had records only for the second parity (P2). The number of daily ET and RUT records in the P1 and P2 cows were 18,569 (on 142 cows) and 34,464 (on 243 cows), respectively. Data on 28,994 SNPs located on 29 Bos taurus autosomes (BTA) of 747 animals (435 males) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 20-SNP sliding window (with an average size of 1.52 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Mean (standard deviation; SD) averaged daily ET and RUT were 327.0 (85.66) and 559.4 (77.69) min/d for cows in P1 and 316.0 (82.24) and 574.2 (75.42) min/d for cows in P2, respectively. Mean (standard deviation; SD) heritability estimates for daily ET and RUT were 0.42 (0.09) and 0.45 (0.06) for cows in P1 and 0.45 (0.04) and 0.43 (0.02) for cows in P2, respectively. Mean (SD) daily genetic correlations between daily ET and RUT were 0.27 (0.07) for P1 and 0.34 (0.08) for P2. Genome-wide association analyses identified 6 genomic regions distributed over 5 chromosomes (BTA1, BTA4, BTA11, 2 regions of BTA14, and BTA17) associated with ET or RUT. The findings of this study increase our preliminary understanding of the genetic background of feeding behavior in dairy cows; however, larger datasets are needed to determine whether ET and RUT might have the potential to be used in selection programs.

Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.

Prediction of key milk biomarkers in dairy cows through milk mid-infrared spectra and international collaborations

At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (β-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-β-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models

The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored. Moreover, many reference measurements of those phenotypes, needed during the FT-MIR calibration step, are available from past research activities but lack spectra records. So, it is impossible to use them to improve the FT-MIR models. Consequently, there is a strong interest in imputing those missing spectra. The innovative objective of this study was to use the existing large spectral DHI database to estimate missing spectra by selecting probable spectra using, as the match criteria, common dairy traits recorded for a long time by DHI organizations. We tested 4 match criteria combinations. Combination 1 required to have equal fat and protein contents between the sample for which a spectrum was to be estimated and the reference samples in the DHI database. Combination 2 also required an equal urea content. Combination 3 requested equal fat, protein, and lactose contents. Finally, combination 4 included all criteria. When more than one spectrum was found during the search, their average was the estimated spectrum for the query sample. Concretely, this study estimated missing spectra for 1,700 samples using 2,000,000 spectral DHI records. For assessing the effect of this spectral estimation on the prediction quality, FT-MIR equations were used to predict 11 phenotypes, selected as their quantification used different FT-MIR regions. They were related to the milk fat and mineral composition, lactoferrin content, quantity of eructed methane, body weight (BW), and dry matter intake. The accuracy between predictions obtained from actual and estimated spectra was evaluated by calculating the mean absolute error (MAE). The criteria in the fourth and second combinations were too strict to estimate a spectrum for most samples. Indeed, for many samples, no spectra with the same values for those matching criteria was found. The third match criteria combination had a poorer prediction performance for all studied traits and spectral absorptions than the first combination due to fewer matched samples available to compute the missing spectrum. By allowing a range for matching lactose content (±0.1 g/dL milk), we showed that this new combination increased the number of selected samples to compute missing spectra and predict better the infrared absorption at different wavenumbers, especially those related to the lactose quantification. The prediction performance was further improved by performing queries on the entire Walloon DHI spectral database (6,625,570 spectra), and it varied among the studied phenotypes. Without considering the traits used for the matching, the best predictions were obtained for the content of saturated fatty acids (MAE = 0.15 g/dL milk) and BW (MAE = 12.80 kg). Yet, the predictions for the unsaturated fatty acids were less accurate (MAE = 0.13 and 0.018 g/dL milk for monounsaturated and polyunsaturated fatty acids), likely because of the poorer predictions of spectral regions related to long-chain fatty acids. Similarly, poorer predictions were observed for the amount of methane eructed by dairy cows (MAE = 47.02 g/d), likely because it is not directly related to fat content or composition. Prediction accuracies for the remaining traits were also low. In conclusion, we observed that increasing the number of relevant matching criteria helps improve the quality of FT-MIR predicted phenotypes and the number of spectra used during the search. So, it would be of great interest to test in the future the suitability of the developed methodology with large-scale international spectral databases to improve the reliability of EBV from these FT-MIR-based phenotypes and the robustness of FT-MIR predictive models.

Defining valid breeding goals for animal breeds

BACKGROUND

The objective of any valid breeding program is to increase the suitability of a breed for its future purposes. The approach most often followed in animal breeding for optimizing breeding goals assumes that the sole desire of the owners is profit maximization. As this assumption is often violated, a generalized approach is needed that does not rely on this assumption.

RESULTS

The generalized approach is based on the niche concept. The niche of a breed is a set of environments in which a small population of the breed would have a positive population growth rate. Its growth rate depends on demand from prospective consumers and supply from producers. The approach involves defining the niche that is envisaged for the breed and identifying the trait optima that maximize the breed's adaptation to its envisaged niche within the set of permissible breeding goals. The set of permissible breeding goals is the set of all potential breeding goals that are compatible with animal welfare and could be reached within the planning horizon of the breeding program. In general, the breed's adaptation depends on the satisfaction of the producers with the animals and on the satisfaction of the consumers with the products produced by the animals. When consumers buy live animals, then the breed needs to adapt to both the environments provided by the producers, and the environments provided by the consumers. The profit function is replaced by a more general adaptedness function that measures the breed's adaptation to its envisaged niche.

CONCLUSIONS

The proposed approach coincides with the traditional approach if the producers have the sole desire to maximize their income, and if consumer preferences are well reflected by the product prices. If these assumptions are not met, then the traditional approach to breeding goal optimization is unlikely to result in a valid breeding goal. Using the example of companion breeds, this paper shows that the proposed approach has the potential to fill the gap.

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.

The use of a genomic relationship matrix for breed assignment of cattle breeds: comparison and combination with a machine learning method

To develop a breed assignment model, three main steps are generally followed: 1) The selection of breed informative SNP; 2) The training of a model, based on a reference population, that allows to classify animals to their breed of origin; and 3) The validation of the developed model on external animals i.e., that were not used in previous steps. However, there is no consensus in the literature about which methodology to follow for the first step, nor about the number of SNP to be selected. This can raise many questions when developing the model and lead to the use of sophisticated methodologies for selecting SNP (e.g., with iterative algorithms, partitions of SNP or combination of several methods). Therefore, it may be of interest to avoid the first step by the use of all the available SNP. For this purpose, we propose the use of a genomic relationship matrix (GRM), combined or not with a machine learning method, for breed assignment. We compared it with a previously developed model based on selected informative SNP. Four methodologies were investigated: 1) The PLS_NSC methodology: selection of SNP based on a partial least square-discriminant analysis (PLS-DA) and breed assignment by classification based on the nearest shrunken centroids (NSC) method; 2) Breed assignment based on the highest mean relatedness of an animal to the reference populations of each breed (referred to mean_GRM); 3) Breed assignment based on the highest SD of the relatedness of an animal to the reference populations of each breed (referred to SD_GRM) and 4) The GRM_SVM methodology: the use of means and SD of the relatedness defined in mean_GRM and SD_GRM methodologies combined with the linear support vector machine (SVM), a machine learning method used for classification. Regarding mean global accuracies, results showed that the use of mean_GRM or GRM_SVM was not significantly different (Bonferroni corrected P > 0.0083) than the model based on a reduced SNP panel (PLS_NSC). Moreover, the mean_GRM and GRM_SVM methodology were more efficient than PLS_NSC as it was faster to compute. Therefore, it is possible to bypass the selection of SNP and, by the use of a GRM, to develop an efficient breed assignment model. In routine, we recommend the use of GRM_SVM over mean_GRM as it gave a slightly increased global accuracy, which can help endangered breeds to be maintained. The script to execute the different methodologies can be accessed on: https : //github.com/hwilmot675/Breed_assignment.

Invited review: From heat stress to disease-Immune response and candidate genes involved in cattle thermotolerance

Heat stress implies unfavorable effects on primary and functional traits in dairy cattle and, in consequence, on the profitability of the whole production system. The increasing number of days with extreme hot temperatures suggests that it is imperative to detect the heat stress status of animals based on adequate measures. However, confirming the heat stress status of an individual is still challenging, and, in consequence, the identification of novel heat stress biomarkers, including molecular biomarkers, remains a very relevant issue. Currently, it is known that heat stress seems to have unfavorable effects on immune system mechanisms, but this information is of limited use in the context of heat stress phenotyping. In addition, there is a lack of knowledge addressing the molecular mechanisms linking the relevant genes to the observed phenotype. In this review, we explored the potential molecular mechanisms explaining how heat stress affects the immune system and, therefore, increases the occurrence of immune-related diseases in cattle. In this regard, 2 relatively opposite hypotheses are under focus: the immunosuppressive action of cortisol, and the proinflammatory effect of heat stress. In both hypotheses, the modulation of the immune response during heat stress is highlighted. Moreover, it is possible to link candidate genes to these potential mechanisms. In this context, immune markers are very valuable indicators for the detection of heat stress in dairy cattle, broadening the portfolio of potential biomarkers for heat stress.

Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle

The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52-2.09 Mb), 26 (9.24-9.66 Mb), 16 (75.41-75.51 Mb), 6 (8.73-88.92 Mb), 6 (8.73-88.92 Mb), 11 (103.26-103.41 Mb), 11 (103.26-103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them.

Estimation of inbreeding, between-breed genomic relatedness and definition of sub-populations in red-pied cattle breeds

Currently, enhancing the collaboration between related breeds is of main importance to increase the competitivity and the sustainability of local breeds. One type of collaboration is the development of an across-breed reference population that will allow a better management of local breeds. For this purpose, the genomic relatedness between the local target breed and possible breeds to be included in the reference population should be estimated. In Europe, there are several local red-pied cattle breeds that would benefit from this kind of collaboration. However, how different red-pied cattle breeds from the Benelux are related to each other and can collaborate is still unclear. The objectives of this study were therefore: (1) to estimate the level of inbreeding of the East Belgian Red and White (EBRW), the Red-Pied of the Ösling (RPO) and Dutch red-pied cattle breeds; (2) to determine the genomic relatedness of several red-pied cattle breeds, with a special focus on two endangered breeds: the EBRW and the RPO, and (3) based on the second objective, to detect animals from other breeds that were genomically close enough to be considered as advantageous in the creation of an across-breed reference population of EBRW or RPO. The estimated inbreeding levels based on runs of homozygosity were relatively low for almost all the studied breeds and especially for the EBRW and RPO. This would imply that inbreeding is currently not an issue in these two endangered breeds and that their sustainability is not threatened by their level of inbreeding. The results from the principal component analysis, the phylogenetic tree and the clustering all highlighted that the EBRW and RPO breeds were included in the genomic continuum of the studied red-pied cattle breeds and can be therefore considered as genomically close to Dutch red-pied cattle breeds, highlighting the possibility of a collaboration between these breeds. Especially, EBRW animals were closely related to Deep Red and Improved Red animals while, to a lesser extent, the RPO animals were closely related to the Meuse-Rhine-Yssel breed. Based on these results, we could use distance measures, based either on the principal component analysis or clustering, to detect animals from Dutch breeds that were genomically closest to the EBRW or RPO breeds. This will finally allow the building of an across-breed reference population for EBRW or RPO for further genomic evaluations, considering these genomically closest animals from other breeds.

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from -0.61 to -0.41 and from -0.55 to -0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models

The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from -0.22 to -0.59), between milk yield and milk FA (rg from -0.22 to -0.62), and between protein yield and milk FA (rg from -0.11 to -0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

Defining a nitrogen efficiency index in Holstein cows and assessing its potential effect on the breeding program of bulls

The purposes of this study were (1) to explore the relationship between 3 milk mid-infrared predicted features including nitrogen intake (NINT), milk true protein N (MTPN), and milk urea-N yield (MUNY); (2) to integrate these 3 features into an N efficiency index (NEI) and analyses approximate genetic correlations between the NEI and 37 traits (indices) of interest; and (3) to assess the potential effect of including the NEI into breeding programs of bulls. The edited data were 1,043,171 test-day records on 342,847 cows in 1,931 herds and 143,595 test-day records on 53,660 cows in 766 herds used for estimating breeding values (EBV) and variance components, respectively. The used records were within 5 to 50 d in milk. The records were grouped into primiparous and multiparous. The genetic parameters for the included mid-infrared features and EBV of the animals included in the pedigree were estimated using a multiple-trait repeatability animal model. Then, the EBV of the NINT, MTPN, MUNY were integrated into the NEI using a selection index assuming weights based on the N partitioning. The approximate genetic correlations between the NEI and 37 traits of interest were estimated using the EBV of the selected bulls. The bulls born from 2011 to 2014 with NEI were selected and the NEI distribution of these bulls having EBV for the 8 selected traits (indices) was checked. The heritability and repeatability estimates for NINT, MTPN, and MUNY ranged from 0.09 to 0.13, and 0.37 to 0.65, respectively. The genetic and phenotypic correlations between NINT, MTPN, and MUNY ranged from -0.31 to 0.87, and -0.02 to 0.42, respectively. The NEI ranged from -13.13 to 12.55 kg/d. In total, 736 bulls with reliability ≥0.50 for all included traits (NEI and 37 traits) and at least 10 daughters distributed in at least 10 herds were selected to investigate genetic aspects of the NEI. The NEI had positive genetic correlations with production yield traits (0.08-0.46), and negative genetic correlations with the investigated functional traits and indices (-0.71 to -0.07), except for the production economic index and functional type economic index. The daughters of bulls with higher NEI had lower NINT and MUNY, and higher MTPN. Furthermore, 26% of the bulls (n = 50) with NEI born between 2011 to 2014 had higher NEI and global economic index than the average in the selected bulls. Finally, the developed NEI has the advantage of large-scale prediction and therefore has the potential for routine application in dairy cattle breeding in the future.

Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk

Simple Summary

Mid-infrared (MIR) spectroscopy is the method of choice to determine milk components like fat, protein and urea. We examined the potential of MIR spectra analyses for the prediction of clinical mastitis events of dairy cows additionally, or alternatively, to somatic cell count, which is routinely used as an indicator for mastitis monitoring. Prediction models based on MIR spectra and a somatic cell count-derived score (SCS) were developed and compared. A model based on MIR spectra and SCS proved more accurate at predicting mastitis than models based on either indicator alone. Consequently, MIR spectra analyses add extra value in the prediction of clinical mastitis, making them potentially useful for dairy farm management and as an auxiliary trait for the genetic evaluation of udder health.

Abstract

Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (−/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health.

Genome-wide association for milk urea concentration in Dual-Purpose Belgian Blue cows

The objectives of this study were to estimate genetic parameters and identify genomic regions associated with milk urea concentration (MU) in Dual-Purpose Belgian Blue (DPBB) cows. The data were 29,693 test-day records of milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP) and MU collected between 2014 and 2020 on 2498 first parity cows (16,935 test-day records) and 1939 second-parity cows (12,758 test-day records) from 49 herds in the Walloon Region of Belgium. Data of 28,266 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), on 1699 animals (639 males and 1060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method using a single chain of 100,000 iterations after a burn-in period of 20,000. SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 consecutive SNPs (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. The mean (SD) of MU was 22.89 (10.07) and 22.35 (10.07) mg/dl for first and second parity, respectively. The mean (SD) heritability estimates for daily MU were 0.18 (0.01) and 0.22 (0.02), for first and second parity, respectively. The mean (SD) genetic correlations between daily MU and MY, FY, PY, FP and PP were -0.05 (0.09), -0.07 (0.11), -0.03 (0.13), -0.05 (0.08) and -0.03 (0.11) for first parity, respectively. The corresponding values estimated for second parity were 0.02 (0.10), -0.02 (0.09), 0.02 (0.08), -0.08 (0.06) and -0.05 (0.05). The genome-wide association analyses identified three genomic regions (BTA2, BTA3 and BTA13) associated with MU. The identified regions showed contrasting results between parities and among different stages within each parity. This suggests that different groups of candidate genes underlie the phenotypic expression of MU between parities and among different lactation stages within a parity. The results of this study can be used for future implementation and use of genomic evaluation to reduce MU in DPBB cows.

Direct In Vivo Analysis of CBD- and THC-Acid Cannabinoids and Classification of Cannabis Cultivars Using SpiderMass

In recent years, cannabis and hemp-based products have become increasingly popular for recreational use, edibles, beverages, health care products, and medicines. The rapid detection and differentiation of phytocannabinoids is, therefore, essential to assess the potency and the therapeutic and nutritional values of cannabis cultivars. Here, we implemented SpiderMass technology for in vivo detection of cannabidiolic acid (CBDA) and ∆9-tetrahydrocannabinolicacid (∆9-THCA), and other endogenous organic plant compounds, to access distribution gradients within the plants and differentiate between cultivars. The SpiderMass system is composed of an IR-laser handheld microsampling probe connected to a mass spectrometer through a transfer tube. The analysis was performed on different plant organs from freshly cultivated cannabis plants in only a few seconds. SpiderMass analysis easily discriminated the two acid phytocannabinoid isomers via MS/MS, and the built statistical models differentiated between four cannabis cultivars. Different abundancies of the two acid phytocannabinoids were found along the plant as well as between different cultivars. Overall, these results introduce direct analysis by SpiderMass as a compelling analytical alternative for rapid hemp analysis.

Identification of chronic stress biomarkers in dairy cows

Stress in dairy herds can occur from multiple sources. When stress becomes chronic because of a long duration and inability of animals to adapt, it is likely to deeply affect the emotional state, health, immunity, fertility and milk production of cows. While assessing chronic stress in herds would be beneficial, no real consensus has emerged from the literature regarding the indicators of interest. The goal of this study was to compare and evaluate potential biomarkers for chronic stress after inducing stress over a 4-week period through severe overstocking, restricted access to feed and isolated unusual events. A total of 30 cows were involved in the experiment and two similar groups were constituted. Over a 4-week period, the 15 cows of the stress group were housed in overstocked conditions, with 4.6 m² per cow, including resting and feeding areas. In this area, only seven individual places at the feeding area were available for the 15 cows to generate competition for feed access. Twice during the trial and over a period of 2 h, an additional stress was induced by moving cows to an unfamiliar barn and diffusion of stressing noises (dog barking). Meanwhile, the 15 cows of the control group stayed in the original barn, with more than 10 m² per cow and more individual places at the feeding area than cow number. On a weekly basis, several variables considered as potential biomarkers for chronic stress were recorded. Collected data were analysed using single trait linear repeated mixed models. No differences were observed regarding milk yield, BW of cows or body condition score but the milk loss was more pronounced in the stress group. The activity was more heterogeneous and the rumination of cows was lower in the stress group. The heart rate was lower in the stress group and showed more heterogeneity at the end of the stress period. No differences were observed regarding salivary cortisol, blood glucose, β-endorphin, thyroxine and leucocyte profile. A higher level of hair cortisol and blood fructosamine were observed in the stress group at the end of the stress period. Regarding the practical use of the highlighted biomarkers, milk loss may be an effective and easy way to detect general problems, including stress. The blood fructosamine and the hair cortisol concentrations are promising indicators to assess chronic stress in commercial farms.

Random regression test-day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows

We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second- to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35 days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

Genome-wide associations for heat stress response suggest potential candidate genes underlying milk fatty acid composition in dairy cattle

Contents of milk fatty acids (FA) display remarkable alterations along climatic gradients. Detecting candidate genes underlying such alterations might be beneficial for the exploration of climate sensitivity in dairy cattle. Consequently, we aimed on the definition of FA heat stress indicators, considering FA breeding values in response to temperature-humidity index (THI) alterations. Indicators were used in GWAS, in ongoing gene annotations and for the estimation of chromosome-wide variance components. The phenotypic data set consisted of 39,600 test-day milk FA records from 5,757 first-lactation Holstein dairy cows kept in 16 large-scale German cooperator herds. The FA traits were C18:0, polyunsaturated fatty acids (PUFA), saturated fatty acids (SFA), and unsaturated fatty acids (UFA). After genotype quality control, 40,523 SNP markers from 3,266 cows and 930 sires were considered. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI, which were allocated to 10 different THI classes. The same FA from 3 stages of lactation were considered as different, but genetically correlated traits. Consequently, a 3-trait reaction norm model was used to estimate genetic parameters and breeding values for FA along THI classes, considering either pedigree (A) or genomic (G) relationship matrices. De-regressed proofs and genomic estimated breeding values at the intermediate THI class 5 and at the extreme THI class 10 were used as pseudophenotypes in ongoing genomic analyses for thermoneutral (TNC) and heat stress conditions (HSC), respectively. The differences in de-regressed proofs and in genomic estimated breeding values from both THI classes were pseudophenotypes for heat stress response (HSR). Genetic correlations between the same FA under TNC and HSC were smallest in the first lactation stage and ranged from 0.20 for PUFA to 0.87 for SFA when modeling with the A matrix, and from 0.35 for UFA to 0.86 for SFA when modeling with the G matrix. In the first lactation stage, larger additive genetic variances under HSC compared with TNC indicate climate sensitivity for C18:0, PUFA, and UFA. Climate sensitivity was also reflected by pronounced chromosome-wide genetic variances for HSR of PUFA and UFA in the first stage of lactation. For all FA under TNC, HSC, and HSR, quite large genetic variance proportions were explained by BTA14. In GWAS, 30 SNP (within or close to 38 potential candidate genes) overlapped for HSR of the different FA. One unique potential candidate gene (AMFR) was detected for HSR of PUFA, 15 for HSR of SFA (ADGRB1, DENND3, DUSP16, EFR3A, EMP1, ENSBTAG00000003838, EPS8, MGP, PIK3C2G, STYK1, TMEM71, GSG1, SMARCE1, CCDC57, and FASN) and 3 for HSR of UFA (ENSBTAG00000048091, PAEP, and EPPK1). The identified unique genes play key roles in milk FA synthesis and are associated with disease resistance in dairy cattle. The results suggest consideration of FA in combination with climatic responses when inferring genetic mechanisms of heat stress in dairy cows.

Farmer and Field Survey in Cassava-Growing Districts of Rwanda Reveals Key Factors Associated With Cassava Brown Streak Disease Incidence and Cassava Productivity

Cassava (Manihot esculenta Crantz) is a vital crop in Rwanda where it ranks as the third most consumed staple. However, cassava productivity remains below its yield potential due to several constraints, including important viral diseases, such as cassava brown streak disease (CBSD). Because various factors can be addressed to mitigate the impact of viral diseases, it is essential to identify routes of virus contamination in the cassava agrosystems from the seed system to farmer's practices and knowledge. The present study aimed at (1) assessing the current cassava seed system and farmers' practices and their knowledge of the biotic constraints to cassava production, (2) determining the status of CBSD as well as critical factors associated with its spread through the seed system channels, and (3) determining factors that influence cassava productivity in Rwanda. A cross-sectional study was carried out from May to September 2019 in 13 districts of Rwanda. A total of 130 farmers and cassava fields were visited, and the incidence and severity of CBSD were evaluated. CBSD was detected in all cassava-producing districts. The highest field incidence of CBSD was recorded in the Nyanza district (62%; 95% CI = 56–67%) followed by the Bugesera district (60%; 95% CI = 54–65%), which recorded the highest severity score of 3.0 ± 0.6. RT-PCR revealed the presence of CBSD at the rate of 35.3%. Ugandan cassava brown streak virus was predominant (21.5%) although cassava brown streak virus was 4% and mixed infection was 10%. An informal cassava seed system was dominant among individual farmers, whereas most cooperatives used quality seeds. Cassava production was found to be significantly influenced by the use of fertilizer, size of the land, farming system, cassava viral disease, and type of cassava varieties grown (p < 0.001). Disease management measures were practiced by a half of participants only. Factors found to be significantly associated with CBSD infection (p < 0.05) were the source of cuttings, proximity to borders, age of cassava, and knowledge of CBSD transmission and management.

The pattern of linkage disequilibrium in Dual-Purpose Belgian Blue cattle

Quantifying the level of linkage disequilibrium (LD), non-random association of alleles at two or more loci, is important to determine the number of markers needed for genomic selection. The aims of this study were to evaluate the extent of LD in Dual-Purpose Belgian Blue (DPBB) and to compare the level of LD in DPBB with that of Walloon Holstein. Data of 28,427 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), of 639 DPBB and 398 Holstein bulls were used. The level of LD between pairwise SNPs separated by up to 10 Mb was evaluated, separately for each breed, using the squared correlation of the alleles at two loci. The analysis of molecular variance showed that the percentage of variation within populations (85.48%) was higher than between populations (14.52%). However, permutation tests showed a significant genetic differentiation between the two studied populations (p < .01). The average LD found between adjacent SNP pairs in DPBB (0.16 (SD = 0.22)) was generally lower than in Holstein (0.23 (SD = 0.27)). The proportion of SNPs in useful LD (r²  > 0.30) within a genomic distance of ≤0.10 Mb between SNPs was 18.58% and 28.23% in DPBB and Holstein bulls, respectively. In both breeds, the effective population size decreased over generations; however, the decline was greater in DPBB than that in Holstein. Based on results, it can be concluded that at least 68,000 SNPs are needed for implementing genomic selection in DPBB cattle with enough accuracy.

Multiple Breeds and Countries' Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.

Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows

The aim of this study was to estimate genetic parameters of milk urea concentration (MU) and its genetic correlations with milk production traits, longevity, and functional traits in the first 3 parities in dairy cows. The edited data set consisted in 9,107,349 MU test-day records from the first 3 parities of 560,739 cows in 2,356 herds collected during the years 1994 to 2020. To estimate the genetic parameters of MU, data of 109 randomly selected herds, with a total of 770,016 MU test-day records, were used. Genetic parameters and estimated breeding values were estimated using a multiple-trait (parity) random regression model. Herd-test-day, age-year-season of calving, and days in milk classes (every 5 d as a class) were used as fixed effects, whereas effects of herd-year of calving, permanent environment, and animal were modeled using random regressions and Legendre polynomials of order 2. The average daily heritability and repeatability of MU during days in milk 5 to 365 in the first 3 parities were 0.19, 0.22, 0.20, and 0.48, 0.48, 0.47, respectively. The mean genetic correlation estimated among MU in the first 3 parities ranged from 0.96 to 0.97. The average daily estimated breeding values for MU of the selected bulls (n = 1,900) ranged from -9.09 to 7.37 mg/dL. In the last 10 yr, the genetic trend of MU has gradually increased. The genetic correlation between MU and 11 traits of interest ranged from -0.28 (milk yield) to 0.28 (somatic cell score). The findings of this study can be used as the first step for development of a routine genetic evaluation for MU and its inclusion into the genetic selection program in the Walloon Region of Belgium.

Investigation on heroin and cocaine quality in Luxembourg

Background

Heroin and cocaine are among the most dangerous illicit drugs available and their presence on the market is increasing. These facts have led to the investigation of the quality of heroin and cocaine samples seized in Luxembourg by police and customs but also collected at the national supervised drug consumption facilities.

Methods

Samples obtained from 2019 to 2020 were analyzed to determine their composition and content using GC–MS, HPLC-UV and LC-Q-ToF. The statistical evaluation of concentration changes depending on the source of collection is based on an ANOVA single factor test and a two-tailed t test.

Results

Results showed important differences between seizure and collection sources. For both drugs, customs samples had significantly higher concentrations than police samples and the latter had significantly higher concentrations than samples from drug consumption facilities, whereas for heroin two cutting steps were identified, for cocaine samples only one appears to occur on the local market. Indeed, cocaine samples seized by police consisted of a mixture of low and high concentration samples.

Conclusion

The results show that extensive adulteration with pharmacological active and inactive compounds takes place at local levels, which, however, are different for heroin and cocaine. This knowledge on variability of quality of drugs should be considered in the elaboration of drug and harm prevention strategies.

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed-informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross-validation, 14 models had a global cross-validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares-discriminant analysis (PLS-DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS-DA method when selecting breed-informative SNPs.

Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra

BACKGROUND

A robust proxy for estimating methane (CH₄ ) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH₄ emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH₄ were developed using a combined dataset including daily CH₄ measurements (n = 1089; g d^-1 ) collected using the SF₆ tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables.

RESULTS

Models developed based on a combined RC and SF₆ dataset predicted the expected pattern in CH₄ values (in g d^-1 ) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R² = 0.68 and standard error = 57 g CH₄ d^-1 ).

CONCLUSIONS

The models developed accounted for more of the observed variability in CH₄ emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry.

Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake

Simple Summary

Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values.

Abstract

We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSE_CV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSE_CIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Climate sensitivity of milk production traits and milk fatty acids in genotyped Holstein dairy cows

The aim of this study was the evaluation of climate sensitivity via genomic reaction norm models [i.e., to infer cow milk production and milk fatty acid (FA) responses on temperature-humidity index (THI) alterations]. Test-day milk traits were recorded between 2010 and 2016 from 5,257 first-lactation genotyped Holstein dairy cows. The cows were kept in 16 large-scale cooperator herds, being daughters of 344 genotyped sires. The longitudinal data consisted of 47,789 test-day records for the production traits milk yield (MY), fat yield (FY), and protein yield (PY), and of 20,742 test-day records for 6 FA including C16:0, C18:0, saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). After quality control of the genotypic data, 41,057 SNP markers remained for genomic analyses. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI. Genomic reaction norm models were applied to estimate genetic parameters in a single-step approach for production traits and FA in dependency of THI at different lactation stages, and to evaluate the model stability. In a first evaluation strategy (New_sire), all phenotypic records from daughters of genotyped sires born after 2010 were masked, to mimic a validation population. In the second strategy (New_env), only daughter records of the new sires recorded in the most extreme THI classes were masked, aiming at predicting sire genomic estimated breeding values (GEBV) under heat stress conditions. Model stability was the correlation between GEBV of the new sires in the reduced data set with respective GEBV estimated from all phenotypic data. Among all test-day production traits, PY responded as the most sensitive to heat stress. As observed for the remaining production traits, genetic variances were quite stable across THI, but genetic correlations between PY from temperate climates with PY from extreme THI classes dropped to 0.68. Genetic variances in dependency of THI were very similar for C16:0 and SFA, indicating marginal climatic sensitivity. In the early lactation stage, genetic variances for C18:0, MUFA, PUFA, and UFA were significantly larger in the extreme THI classes compared with the estimates under thermoneutral conditions. For C18:0 and MUFA, PUFA, and UFA in the middle THI classes, genetic correlations in same traits from the early and the later lactation stages were lower than 0.50, indicating strong days in milk influence. Interestingly, within lactation stages, genetic correlations for C18:0 and UFA recorded at low and high THI were quite large, indicating similar genetic mechanisms under stress conditions. The model stability was improved when applying the New_env instead of New_sire strategy, especially for FA in the first stage of lactation. Results indicate moderately accurate genomic predictions for milk traits in extreme THI classes when considering phenotypic data from a broad range of remaining THI. Phenotypically, thermal stress conditions contributed to an increase of UFA, suggesting value as a heat stress biomarker. Furthermore, the quite large genetic variances for UFA at high THI suggest the consideration of UFA in selection strategies for improved heat stress resistance.

Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows

A routine monitoring for subacute ruminal acidosis (SARA) on the individual level could support the minimization of economic losses and the ensuring of animal welfare in dairy cows. The objectives of this study were (1) to develop a SARA risk score (SRS) by combining information from different data acquisition systems to generate an integrative indicator trait, (2) the investigation of associations of the SRS with feed analysis data, blood characteristics, performance data, and milk composition, including the fatty acid (FA) profile, (3) the development of a milk mid-infrared (MIR) spectra-based prediction equation for this novel reference trait SRS, and (4) its application to an external data set consisting of MIR data of test day records to investigate the association between the MIR-based predictions of the SRS and the milk FA profile. The primary data set, which was used for the objectives (1) to (3), consisted of data collected from 10 commercial farms with a total of 100 Holstein cows in early lactation. The data comprised barn climate parameters, pH and temperature logging from intrareticular measurement boluses, as well as jaw movement and locomotion behavior recordings of noseband-sensor halters and pedometers. Further sampling and data collection included feed samples, blood samples, milk performance, and milk samples, whereof the latter were used to get the milk MIR spectra and to estimate the main milk components, the milk FA profile, and the lactoferrin content. Because all measurements were characterized by different temporal resolutions, the data preparation consisted of an aggregation into values on a daily basis and merging it into one data set. For the development of the SRS, a total of 7 traits were selected, which were derived from measurements of pH and temperature in the reticulum, chewing behavior, and milk yield. After adjustment for fixed effects and standardization, these 7 traits were combined into the SRS using a linear combination and directional weights based on current knowledge derived from literature studies. The secondary data set was used for objective (4) and consisted of test day records of the entire herds, including performance data, milk MIR spectra and MIR-predicted FA. At farm level, it could be shown that diets with higher proportions of concentrated feed resulted in both lower daily mean pH and higher SRS values. On the individual level, an increased SRS could be associated with a modified FA profile (e.g., lower levels of short- and medium-chain FA, higher levels of C17:0, odd- and branched-chain FA). Furthermore, a milk MIR-based partial least squares regression model with a moderate predictability was established for the SRS. This work provides the basis for the development of routine SARA monitoring and demonstrates the high potential of milk composition-based assessment of the health status of lactating cows.

Estimation of genetic parameters for predicted nitrogen use efficiency and losses in early lactation of Holstein cows

The objective of this study was to estimate genetic parameters of predicted N use efficiency (PNUE) and N losses (PNL) as proxies of N use and loss for Holstein cows. Furthermore, we have assessed approximate genetic correlations between PNUE, PNL, and dairy production, health, longevity, and conformation traits. These traits are considered important in many countries and are currently evaluated by the International Bull Evaluation Service (Interbull). The values of PNUE and PNL were obtained by using the combined milk mid-infrared (MIR) spectrum, parity, and milk yield-based prediction equations on test-day MIR records with days in milk (DIM) between 5 and 50 d. After editing, the final data set comprised 46,163 records of 21,462 cows from 154 farms in 5 countries. Each trait was divided into primiparous and multiparous (including second to fifth parity) groups. Genetic parameters and breeding values were estimated by using a multitrait (2-trait, 2-parity classes) repeatability model. Herd-year-season of calving, DIM, age of calving, and parity were used as fixed effects. Random effects were defined as parity (within-parity permanent environment), nongenetic cow (across-parity permanent environment), additive genetic animal, and residual effects. The estimated heritability of PNUE and PNL in the first and later parity were 0.13, 0.12, 0.14, and 0.13, and the repeatability values were 0.49, 0.40, 0.55, and 0.43, respectively. The estimated approximate genetic correlations between PNUE and PNL were negative and high (from -0.89 to -0.53), whereas the phenotypic correlations were also negative but relatively low (from -0.45 to -0.11). At a level of reliability of more than 0.30 for all novel traits, a total of 504 bulls born after 1995 had also publishable Interbull multiple-trait across-country estimated breeding values (EBV). The approximate genetic correlations between PNUE and the other 30 traits of interest, estimated as corrected correlations between EBV of bulls, ranged from -0.46 (udder depth) to 0.47 (milk yield). Obtained results showed the complex genetic relationship between efficiency, production, and other traits: for instance, more efficient cows seem to give more milk, which is linked to deeper udders, but seem to have lower health, fertility, and longevity. Additionally, the approximate genetic correlations between PNL, lower values representing less loss of N, and the 30 other traits, were from -0.32 (angularity) to 0.57 (direct calving ease). Even if further research is needed, our results provided preliminary evidence that the PNUE and PNL traits used as proxies could be included in genetic improvement programs in Holstein cows and could help their management.

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

Lactoferrin (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.

Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions

Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The second objective is to emphasize methods and indicators to evaluate the quality of models and the quality of predictions generated under routine conditions. The last objective is to outline the issues and the solutions linked with the use and transfer of models on large number of instruments. Based on partial least square regression and 10 datasets including milk MIR spectra and reference quantitative values for 57 traits of interest, the impact of the different factors is illustrated by evaluating the influence on the validation root mean square error of prediction (RMSEP). In the displayed examples, all factors, when well set up, increase the quality of predictions, with an improvement of the RMSEP ranging from 12% to 43%. This work also aims to underline the need for and the complementarity between different validation procedures, statistical parameters and quality assurance methods. Finally, when using and transferring models, the impact of the spectral standardization on the prediction reproducibility is highlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions.

Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra?

Phenotypes related to feed efficiency were predicted from records easily acquired by breeding organizations. A total of 461,036 and 354,148 records were collected from the first and second parity Holstein cows. Equations were applied to the milk mid-infrared spectra to predict the main milk components and coupled with animal characteristics to predict the body weight (pBW). Dry matter intake (pDMI) was predicted from pBW using the National Research Council (NRC) equation. The consumption index (pIC) was estimated from pDMI and fat, and protein corrected milk. All traits were modeled using single trait test-day models. Descriptive statistics were within the expected range. Milk yield, pDMI, and pBW were phenotypically positively related (r ranged from 0.08 to 0.64). As expected, pIC was phenotypically negatively correlated with milk yield (-0.77 and -0.80 for the first and second lactation) and slightly positively correlated with pBW (0.16 and 0.07 for the first and second lactation). Later, parity cows seemed to have a better feed efficiency as they had a lower pIC. Although the prediction accuracy was moderate, the observed behaviors of studied traits by year, stage of lactation, and parity were in agreement with the literature. Moreover, as a genetic component was highlighted (heritability around 0.18), it would be interesting to realize a genetic evaluation of these traits and compare the obtained breeding values with the ones estimated for sires having daughters with reference feed efficiency records.

Predicting milk mid-infrared spectra from first-parity Holstein cows using a test-day mixed model with the perspective of herd management

The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems.

Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.

Short communication: Investigation of the temporal relationships between milk mid-infrared predicted biomarkers and lameness events in later lactation

This study reports on the exploration of temporal relationships between milk mid-infrared predicted biomarkers and lameness events. Lameness in dairy cows is an issue that can vary greatly in severity and is of concern for both producers and consumers. Metabolic disorders are often associated with lameness. However, lameness can arise weeks or even months after the metabolic disorder, making the detection of causality difficult. We already use mid-infrared technology to predict major milk components, such as fat or protein, during routine milk recording and for milk payment. It was recently shown that this technology can also be used to predict novel biomarkers linked to metabolic disorders in cows, such as oleic acid (18:1 cis-9), β-hydroxybutyrate, acetone, and citrate in milk. We used these novel biomarkers as proxies for metabolic issues. Other studies have explored the possibility of using mid-infrared spectra to predict metabolic diseases and found it (potentially) usable for indicating classes of metabolic problems. We wanted to explore the possible relationship between mid-infrared-based metabolites and lameness over the course of lactation. In total, data were recorded from 6,292 cows on 161 farms in Austria. Lameness data were recorded between March 2014 and March 2015 and consisted of 37,555 records. Mid-infrared data were recorded between July and December 2014 and consisted of 9,152 records. Our approach consisted of fitting preadjustments to the data using fixed effects, computing pair-wise correlations, and finally applying polynomial smoothing of the correlations for a given biomarker at a certain month in lactation and the lameness events scored on severity scale from sound or non-lame (lameness score of 1) to severely lame (lameness score of 5) throughout the lactation. The final correlations between biomarkers and lameness scores were significant, but not high. However, for the results of the present study, we should not look at the correlations in terms of absolute values, but rather as indicators of a relationship through time. When doing so, we can see that metabolic problems occurring in mo 1 and 3 seem more linked to long-term effects on hoof and leg health than those in mo 2. However, the quantity (only 1 pair-wise correlation exceeded 1,000 observations) and the quality (due to limited data, no separation according to more metabolic-related diseases could be done) of the data should be improved.

Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?

Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion.