1
|
Ablondi M, Summer A, Stocco G, Degano L, Vicario D, Stefanon B, Sabbioni A, Cipolat-Gotet C. Heritability and genetic correlations of total and differential somatic cell count with milk yield and composition traits in Italian Simmental cows. J Dairy Sci 2023; 106:9071-9077. [PMID: 37641255 DOI: 10.3168/jds.2023-23639] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 06/27/2023] [Indexed: 08/31/2023]
Abstract
Costs of production have deeply increased each year in the last decades, breeders are continuously looking for more cost effective and more efficient ways to produce milk. Despite the major signs of progress in productivity, it is fundamental to optimize rather than maximize the performances of the dairy cows. Mastitis is still a highly prevalent disease in the dairy sector which causes several economic losses and environmental effect. Its accurate and early diagnosis is crucial to improve profitability of dairy cows and contribute to a more sustainable dairy industry. Among mastitis reduction strategies, there is the urgent need to implement breeding objectives to select cows displaying mastitis resistance by investigating the genetic mechanisms at the base of the inflammatory response. Therefore, in this study we aimed to further understand the genetic background of the differential somatic cell count (DSCC), which provides thorough insights on the actual inflammatory status of the mammary glands. The objectives of this study were to estimate on a cohort of 20,215 Italian Simmental cows over a 3-yr period: (1) the heritability and repeatability values of somatic cell score (SCS) and DSCC, (2) the genetic and phenotypic correlations between these 2 traits and milk production and milk composition traits, (3) the heritability and repeatability values of SCS and DSCC within class of udder health status. Heritability was low both for SCS (0.06) and DSCC (0.08), whereas the repeatability values for these traits were 0.43 and 0.36, suggesting that the magnitude of cow permanent environmental effect for these traits is remarkable. The genetic and phenotypic correlation of SCS with DSCC was 0.612 and 0.605, respectively. Because both significantly differed from the unit, we must consider those traits as different ones. This latter aspect corroborates the need to consider the DSCC as a further indicator of inflammatory status which might be implemented in the Simmental breed genetic evaluation. It is worthy to mention that heritability estimates for SCS and DSCC were the highest in healthy cows compared with the other udder health classes. This implies that when the udder health status changes, it is most likely due to environmental factors rather than aspects related to the animal's genetics. In contrast, the highest additive genetic variance and heritability found for SCS and DSCC in the healthy group might reveal the potential to further implement breeding strategies to select for healthier animals.
Collapse
Affiliation(s)
- Michela Ablondi
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Andrea Summer
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Giorgia Stocco
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
| | - Lorenzo Degano
- Associazione Nazionale Allevatori Bovini di Razza Pezzata Rossa Italiana, 33100 Udine, Italy
| | - Daniele Vicario
- Associazione Nazionale Allevatori Bovini di Razza Pezzata Rossa Italiana, 33100 Udine, Italy
| | - Bruno Stefanon
- Department of Agri-Food, Environmental and Animal Sciences, University of Udine, 33100 Udine, Italy
| | - Alberto Sabbioni
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | | |
Collapse
|
2
|
Macedo Mota LF, Bisutti V, Vanzin A, Pegolo S, Toscano A, Schiavon S, Tagliapietra F, Gallo L, Ajmone Marsan P, Cecchinato A. Predicting milk protein fractions using infrared spectroscopy and a gradient boosting machine for breeding purposes in Holstein cattle. J Dairy Sci 2023; 106:1853-1873. [PMID: 36710177 DOI: 10.3168/jds.2022-22119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 10/10/2022] [Indexed: 01/29/2023]
Abstract
In recent years, increasing attention has been focused on the genetic evaluation of protein fractions in cow milk with the aim of improving milk quality and technological characteristics. In this context, advances in high-throughput phenotyping by Fourier transform infrared (FTIR) spectroscopy offer the opportunity for large-scale, efficient measurement of novel traits that can be exploited in breeding programs as indicator traits. We took milk samples from 2,558 Holstein cows belonging to 38 herds in northern Italy, operating under different production systems. Fourier transform infrared spectra were collected on the same day as milk sampling and stored for subsequent analysis. Two sets of data (i.e., phenotypes and FTIR spectra) collected in 2 different years (2013 and 2019-2020) were compiled. The following traits were assessed using HPLC: true protein, major casein fractions [αS1-casein (CN), αS2-CN, β-CN, κ-CN, and glycosylated-κ-CN], and major whey proteins (β-lactoglobulin and α-lactalbumin), all of which were measured both in grams per liter (g/L) and proportion of total nitrogen (% N). The FTIR predictions were calculated using the gradient boosting machine technique and tested by 3 different cross-validation (CRV) methods. We used the following CRV scenarios: (1) random 10-fold, which randomly split the whole into 10-folds of equal size (9-folds for training and 1-fold for validation); (2) herd/date-out CRV, which assigned 80% of herd/date as the training set with independence of 20% of herd/date assigned as the validation set; (3) forward/backward CRV, which split the data set in training and validation set according with the year of milk sampling (FTIR and gold standard data assessed in 2013 or 2019-2020) using the "old" and "new" databases for training and validation, and vice-versa with independence among them; (4) the CRV for genetic parameters (CRV-gen), where animals without pedigree as assigned as a fixed training population and animals with pedigree information was split in 5-folds, in which 1-fold was assigned to the fixed training population, and 4-folds were assigned to the validation set (independent from the training set). The results (i.e., measures and predictions) of CRV-gen were used to infer the genetic parameters for gold standard laboratory measurements (i.e., proteins assessed with HPLC) and FTIR-based predictions considering the CRV-gen scenario from a bi-trait animal model using single-step genomic BLUP. We found that the prediction accuracies of the gradient boosting machine equations differed according to the way in which the proteins were expressed, achieving higher accuracy when expressed in g/L than when expressed as % N in all CRV scenarios. Concerning the reproducibility of the equations over the different years, the results showed no relevant differences in predictive ability between using "old" data as the training set and "new" data as the validation set and vice-versa. Comparing the additive genetic variance estimates for milk protein fractions between the FTIR predicted and HPLC measures, we found reductions of -19.7% for milk protein fractions expressed in g/L, and -21.19% expressed as % N. Although we found reductions in the heritability estimates, they were small, with values ranging from -1.9 to -7.25% for g/L, and -1.6 to -7.9% for % N. The posterior distributions of the additive genetic correlations (ra) between the FTIR predictions and the laboratory measurements were generally high (>0.8), even when the milk protein fractions were expressed as % N. Our results show the potential of using FTIR predictions in breeding programs as indicator traits for the selection of animals to enhance milk protein fraction contents. We expect acceptable responses to selection due to the high genetic correlations between HPLC measurements and FTIR predictions.
Collapse
Affiliation(s)
- L F Macedo Mota
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - V Bisutti
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - A Vanzin
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - S Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy.
| | - A Toscano
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - S Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - F Tagliapietra
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - L Gallo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - P Ajmone Marsan
- Department of Animal Science, Food and Nutrition (DIANA) and Research Center Romeo and Enrica Invernizzi for Sustainable Dairy Production (CREI), Faculty of Agricultural, Food and Environmental Sciences, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| |
Collapse
|
3
|
Mariani E, Malacarne M, Cipolat-Gotet C, Cecchinato A, Bittante G, Summer A. Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows. Front Vet Sci 2022; 9:1012251. [PMID: 36311669 PMCID: PMC9606222 DOI: 10.3389/fvets.2022.1012251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 11/04/2022] Open
Abstract
The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CYCURD), total solids and water retained in the fresh cheese (%CYSOLIDS and %CYWATER), and 60-days ripened cheese (%CYRIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.
Collapse
Affiliation(s)
- Elena Mariani
- Department of Veterinary Science, University of Parma, Parma, Italy
| | | | - Claudio Cipolat-Gotet
- Department of Veterinary Science, University of Parma, Parma, Italy,*Correspondence: Claudio Cipolat-Gotet
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Legnaro, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Legnaro, Italy
| | - Andrea Summer
- Department of Veterinary Science, University of Parma, Parma, Italy
| |
Collapse
|
4
|
Amalfitano N, Macedo Mota LF, Rosa GJM, Cecchinato A, Bittante G. Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile. J Dairy Sci 2022; 105:6001-6020. [PMID: 35525618 DOI: 10.3168/jds.2021-21421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/28/2022] [Indexed: 11/19/2022]
Abstract
To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (αS1-CN, αS2-CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the β-CN, κ-CN, and β-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the β-LG B variant was associated with a lower concentration of β-LG compared with the β-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the β-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health.
Collapse
Affiliation(s)
- Nicolò Amalfitano
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Lucio Flavio Macedo Mota
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Guilherme J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison 53706
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| |
Collapse
|
5
|
Bittante G, Patel N, Cecchinato A, Berzaghi P. Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese. J Dairy Sci 2022; 105:1817-1836. [PMID: 34998561 DOI: 10.3168/jds.2021-20640] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/26/2021] [Indexed: 11/19/2022]
Abstract
Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.
Collapse
Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), 35020 Legnaro, Italy
| | - Nageshvar Patel
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), 35020 Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), 35020 Legnaro, Italy.
| | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health (MAPS), University of Padova (Padua), 35020 Legnaro, Italy
| |
Collapse
|
6
|
Bittante G, Amalfitano N, Bergamaschi M, Patel N, Haddi ML, Benabid H, Pazzola M, Vacca GM, Tagliapietra F, Schiavon S. Composition and aptitude for cheese-making of milk from cows, buffaloes, goats, sheep, dromedary camels, and donkeys. J Dairy Sci 2021; 105:2132-2152. [PMID: 34955249 DOI: 10.3168/jds.2021-20961] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/04/2021] [Indexed: 12/20/2022]
Abstract
Bovines account for about 83% of the milk and dairy products consumed by humans worldwide, the rest represented by bubaline, caprine, ovine, camelid, and equine species, which are particularly important in areas of extensive pastoralism. Although milk is increasingly used for cheese production, the cheese-making efficiency of milk from the different species is not well known. This study compares the cheese-making ability of milk sampled from lactating females of the 6 dairy species in terms of milk composition, coagulation properties (using lactodynamography), curd-firming modeling, nutrients recovered in the curd, and cheese yield (through laboratory model-cheese production). Equine (donkey) milk had the lowest fat and protein content and did not coagulate after rennet addition. Buffalo and ewe milk yielded more fresh cheese (25.5 and 22.9%, respectively) than cow, goat, and dromedary milk (15.4, 11.9, and 13.8%, respectively). This was due to the greater fat and protein contents of the former species with respect to the latter, but also to the greater recovery of fat in the curd of bubaline (88.2%) than in the curd of camelid milk (55.0%) and consequent differences in the recoveries of milk total solids and energy in the curd; protein recovery, however, was much more similar across species (from 74.7% in dromedaries to 83.7% in bovine milk). Compared with bovine milk, the milk from the other Artiodactyla species coagulated more rapidly, reached curd firmness more quickly (especially ovine milk), had a more pronounced syneresis (especially caprine milk), had a greater potential asymptotical curd firmness (except dromedary and goat milk), and reached earlier maximum curd firmness (especially caprine and ovine milk). The maximum measured curd firmness was greater for bubaline and ovine milk, intermediate for bovine and caprine milk, and lower for camelid milk. The milk of all ruminant species can be used to make cheese, but, to improve efficiency, cheese-making procedures need to be optimized to take into account the large differences in their coagulation, curd-firming, and syneresis properties.
Collapse
Affiliation(s)
- Giovanni Bittante
- DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Nicolò Amalfitano
- DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Matteo Bergamaschi
- DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Nageshvar Patel
- DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Mohamed-Laid Haddi
- Laboratoire de Mycologie, Biotechnologie et Activité Microbienne, Université des Frères Mentouri, Constantine 25000, Algeria
| | - Hamida Benabid
- Institut de Nutrition, Alimentation et Technologies Agro-Alimentaires, Université des Frères Mentouri, Constantine 25000, Algeria
| | - Michele Pazzola
- Department of Animal Biology, University of Sassari, 07100 Sassari, Italy
| | | | - Franco Tagliapietra
- DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Stefano Schiavon
- DAFNAE-Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova (Padua), 35020 Legnaro (PD), Italy
| |
Collapse
|
7
|
Bittante G, Cecchinato A, Tagliapietra F, Schiavon S, Toledo-Alvarado H. Effects of breed, farm intensiveness, and cow productivity level on cheese-making ability predicted using infrared spectral data at the population level. J Dairy Sci 2021; 104:11790-11806. [PMID: 34389149 DOI: 10.3168/jds.2021-20499] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/30/2021] [Indexed: 11/19/2022]
Abstract
Fourier-transform infrared (FTIR) spectra collected during milk recording schemes at population level can be used for predicting novel traits of interest for farm management, cows' genetic improvement, and milk payment systems. The aims of this study were as follows. (1) To predict cheese yield traits using FTIR spectra from routine milk recordings exploiting previously developed calibration equations. (2) To compare the predicted cheese-making abilities of different dairy and dual-purpose breeds. (3) To analyze the effects of herds' level of intensiveness (HL) and of the cow's level of productivity (CL). (4) To compare the patterns of predicted cheese yields with the patterns of milk composition in different breeds to discern the drivers of cheese-making efficiency. The major sources of variation of FTIR predictions of cheese yield ability (fresh cheese or cheese solids produced per unit milk) of individual milk samples were studied on 115,819 cows of 4 breeds (2 specialized dairy breeds, Holstein and Brown Swiss, and 2 dual-purpose breeds, Simmental and Alpine Grey) from 6,430 herds and exploiting 1,759,706 FTIR test-day spectra collected over 7 yr of milk sampling. Calibration equations used were previously developed on 1,264 individual laboratory model cheese procedures (cross-validation R2 0.85 and 0.95 for fresh and solids cheese yields, respectively). The linear model used for statistical analysis included the effects of parity, lactation stage, year of calving, month of sampling, HL, CL, breed of cow, and the interactions breed × HL and breed × CL. The HL and CL stratifications (5 classes each) were based on average daily secretion of milk net energy per cow. All effects were highly significant (P < 0.001). The major conclusions were as follows. (1) The FTIR-based prediction of cheese yield of milk goes beyond the knowledge of fat and protein content, partially explaining differences in cheese-making ability in different cows, breeds and herds. (2) Differences in cheese yields of different breeds are only partially explained by milk fat and protein composition, and less productive breeds are characterized by a higher milk nutrient content as well as a higher recovery of nutrients in the cheese. (3) High-intensive herds not only produce much more milk, but the milk has a higher nutrient content and a higher cheese yield, whereas within herds, compared with less productive cows, the more productive cows have a much greater milk yield, milk with a greater content of fat but not of protein, and a moderate improvement in cheese yield, differing little from expectations based on milk composition. Finally, (4) the effects of HL and CL on milk quality and cheese-making ability are similar but not identical in different breeds, the less productive ones having some advantage in terms of cheese-making ability. We can obtain FTIR-based prediction of cheese yield from individual milk samples retrospectively at population level, which seems to go beyond the simple knowledge of milk composition, incorporating information on nutrient retention ability in cheese, with possible advantages for management of farms, genetic improvement of dairy cows, and milk payment systems.
Collapse
Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Franco Tagliapietra
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, National Autonomous University of Mexico, Ciudad Universitaria, 04510 Mexico City, Mexico
| |
Collapse
|
8
|
Tiplady KM, Lopdell TJ, Reynolds E, Sherlock RG, Keehan M, Johnson TJJ, Pryce JE, Davis SR, Spelman RJ, Harris BL, Garrick DJ, Littlejohn MD. Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle. Genet Sel Evol 2021; 53:62. [PMID: 34284721 PMCID: PMC8290608 DOI: 10.1186/s12711-021-00648-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 06/22/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk. RESULTS Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk. CONCLUSIONS This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.
Collapse
Affiliation(s)
- Kathryn M. Tiplady
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - Thomas J. Lopdell
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Edwardo Reynolds
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - Richard G. Sherlock
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Michael Keehan
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Thomas JJ. Johnson
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Jennie E. Pryce
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Stephen R. Davis
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Richard J. Spelman
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Bevin L. Harris
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Dorian J. Garrick
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - Mathew D. Littlejohn
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| |
Collapse
|
9
|
Pegolo S, Mota LFM, Bisutti V, Martinez-Castillero M, Giannuzzi D, Gallo L, Schiavon S, Tagliapietra F, Revello Chion A, Trevisi E, Negrini R, Ajmone Marsan P, Cecchinato A. Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows. J Dairy Sci 2021; 104:10934-10949. [PMID: 34253356 DOI: 10.3168/jds.2021-20395] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 05/17/2021] [Indexed: 01/07/2023]
Abstract
Mastitis is one of the most prevalent diseases in dairy cattle and is the cause of considerable economic losses. Alongside somatic cell count (SCC), differential somatic cell count (DSCC) has been recently introduced as a new indicator of intramammary infection. The DSCC is expressed as a count or a proportion (%) of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in milk somatic cells. These numbers are complemented to total somatic cell count or to 100 by macrophages (MAC). The aim of this study was to investigate the genetic variation and heritability of DSCC, and its correlation with milk composition, udder health indicators, milk composition, and technological traits in Holstein cattle. Data used in the analysis consisted in single test-day records from 2,488 Holstein cows reared in 36 herds located in northern Italy. Fourier-transform infrared (FTIR) spectroscopy was used to predict missing information for some milk coagulation and cheese-making traits, to increase sample size and improve estimation of the genetic parameters. Bayesian animal models were implemented via Gibbs sampling. Marginal posterior means of the heritability estimates were 0.13 for somatic cell score (SCS); 0.11 for DSCC, MAC proportion, and MAC count; and 0.10 for PMN-LYM count. Posterior means of additive genetic correlations between SCS and milk composition and udder health were low to moderate and unfavorable. All the relevant genetic correlations between the SCC traits considered and the milk traits (composition, coagulation, cheese yield and nutrients recovery) were unfavorable. The SCS showed genetic correlations of -0.30 with the milk protein proportion, -0.56 with the lactose proportion and -0.52 with the casein index. In the case of milk technological traits, SCS showed genetic correlations of 0.38 with curd firming rate (k20), 0.45 with rennet coagulation time estimated using the curd firming over time equation (RCTeq), -0.39 with asymptotic potential curd firmness, -0.26 with maximum curd firmness (CFmax), and of -0.31 with protein recovery in the curd. Differential somatic cell count expressed as proportion was correlated with SCS (0.60) but had only 2 moderate genetic correlations with milk traits: with lactose (-0.32) and CFmax (-0.33). The SCS was highly correlated with the log PMN-LYM count (0.79) and with the log MAC count (0.69). The 2 latter traits were correlated with several milk traits: fat (-0.38 and -0.43 with PMN-LYM and MAC counts, respectively), lactose percentage (-0.40 and -0.46), RCTeq (0.53 and 0.41), tmax (0.38 and 0.48). Log MAC count was correlated with k20 (+0.34), and log PMN-LYM count was correlated with CFmax (-0.26) and weight of water curd as percentage of weight of milk processed (-0.26). The results obtained offer new insights into the relationships between the indicators of udder health and the milk technological traits in Holstein cows.
Collapse
Affiliation(s)
- S Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy.
| | - L F M Mota
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - V Bisutti
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - M Martinez-Castillero
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - D Giannuzzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - L Gallo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - S Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - F Tagliapietra
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| | - A Revello Chion
- Associazione Regionale Allevatori del Piemonte, Via Torre Roa, 13, 12100 Cuneo, Italy
| | - E Trevisi
- Department of Animal Science, Food and Nutrition - DIANA, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy; Romeo and Enrica Invernizzi Research Center for Sustainable Dairy Production of the Università Cattolica del Sacro Cuore (CREI), 29122 Piacenza, Italy
| | - R Negrini
- Department of Animal Science, Food and Nutrition - DIANA, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy; Italian Association of Breeders (AIA), 00161 Rome, Italy
| | - P Ajmone Marsan
- Department of Animal Science, Food and Nutrition - DIANA, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy; Nutrigenomics and Proteomics Research Center - PRONUTRIGEN, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020 Legnaro PD, Italy
| |
Collapse
|
10
|
Bittante G, Savoia S, Cecchinato A, Pegolo S, Albera A. Phenotypic and genetic variation of ultraviolet-visible-infrared spectral wavelengths of bovine meat. Sci Rep 2021; 11:13946. [PMID: 34230594 PMCID: PMC8260661 DOI: 10.1038/s41598-021-93457-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/22/2021] [Indexed: 01/07/2023] Open
Abstract
Spectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet-visible and near-infrared region (UV-Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV-Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV-Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.
Collapse
Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy
| | - Simone Savoia
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy.,Associazione Nazionale Allevatori Bovini di Razza Piemontese, Strada Trinità 32/A, 12061, Carrù, CN, Italy.,Department of Animal Breeding and Genetics, Interbull Centre, SLU, PO Box 7023, 750 07, Uppsala, Sweden
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy
| | - Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy.
| | - Andrea Albera
- Associazione Nazionale Allevatori Bovini di Razza Piemontese, Strada Trinità 32/A, 12061, Carrù, CN, Italy
| |
Collapse
|
11
|
Martinez-Castillero M, Pegolo S, Sartori C, Toledo-Alvarado H, Varona L, Degano L, Vicario D, Finocchiaro R, Bittante G, Cecchinato A. Genetic correlations between fertility traits and milk composition and fatty acids in Holstein-Friesian, Brown Swiss, and Simmental cattle using recursive models. J Dairy Sci 2021; 104:6832-6846. [PMID: 33773778 DOI: 10.3168/jds.2020-19694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022]
Abstract
This study aimed to investigate the genetic and putative causal relationships between fertility traits [i.e., days open (DO) and calving rate (CR)] and milk quality, composition, and fatty acid contents (milk composition traits) in Holstein-Friesian, Brown Swiss, and Simmental cattle, using recursive models within a Bayesian framework. Trivariate animal models were run, each including one fertility trait, one milk composition trait, and, in all models, milk yield. The DO and CR data were merged with the test days closest to the insemination date for milk composition traits. After editing, 16,468 to 23,424 records for Holstein-Friesian, 23,424 to 46,660 for Brown Swiss, and 26,105 to 35,574 for Simmental were available for the analyses. Recursive animal models were applied to investigate the possible causal influences of milk composition traits on fertility and the genetic relationships among these traits. The results suggested a potential cause-and-effect relationship between milk composition traits and fertility traits, with the first trait influencing the latter. We also found greater recursive effects of milk composition traits on DO than on CR, the latter with some putative differences among breeds in terms of sensitivity. For instance, the putative causal effects of somatic cell score on CR (on the observed scale, %) varied from -0.96 to -1.39%, depending on the breed. Concerning fatty acids, we found relevant putative effects of C18:0 on CR, with estimates varying from -7.8 to -9.9%. Protein and casein percentages, and short-chain fatty acid showed larger recursive effects on CR, whereas fat, protein, and casein percentages, C16:0, C18:0, and long-chain fatty acid had larger effects on DO. The results obtained suggested that these milk traits could be considered as effective indicators of the effects of animal metabolic and physiological status on fertility.
Collapse
Affiliation(s)
- M Martinez-Castillero
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell' Università 16 35020, Legnaro PD, Italy
| | - S Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell' Università 16 35020, Legnaro PD, Italy.
| | - C Sartori
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell' Università 16 35020, Legnaro PD, Italy
| | - H Toledo-Alvarado
- Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Ciudad Universitaria 3000, Mexico City 04510, Mexico
| | - L Varona
- Unidad de Genética Cuantitativa y Mejora Animal, Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, Calle de Miguel Servet 177, 50013 Zaragoza, Spain
| | - L Degano
- Associazione Nazionale Allevatori Razza Pezzata Rossa Italiana (ANAPRI), Udine 33100, Italy
| | - D Vicario
- Associazione Nazionale Allevatori Razza Pezzata Rossa Italiana (ANAPRI), Udine 33100, Italy
| | - R Finocchiaro
- Associazione Nazionale Allevatori bovini della razza Frisona e Jersey Italiana (ANAFIJ), Via Bergamo 292, 26100 Cremona, Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell' Università 16 35020, Legnaro PD, Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell' Università 16 35020, Legnaro PD, Italy
| |
Collapse
|
12
|
Savoia S, Albera A, Brugiapaglia A, Di Stasio L, Cecchinato A, Bittante G. Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory-measured traits. J Anim Sci Biotechnol 2021; 12:29. [PMID: 33706809 PMCID: PMC7953783 DOI: 10.1186/s40104-021-00555-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 01/12/2021] [Indexed: 02/06/2023] Open
Abstract
Background The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions. Results The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R2 from 0.52 to 0.80), low for pH and purge losses (R2 around 0.30), and very poor for cooking losses and tenderness (R2 below 0.20). Except for lightness and purge losses, the heritability estimates of most of the predicted traits were lower than those of the measured traits while the genetic correlations between measured and predicted traits were high (average value 0.81). Conclusions Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.
Collapse
Affiliation(s)
- Simone Savoia
- Associazione Nazionale Allevatori Bovini di Razza Piemontese, strada provinciale Trinita' 32/A, 12061, Carrù, CN, Italy. .,Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy.
| | - Andrea Albera
- Associazione Nazionale Allevatori Bovini di Razza Piemontese, strada provinciale Trinita' 32/A, 12061, Carrù, CN, Italy
| | - Alberto Brugiapaglia
- Department of Agricultural, Forest and Food Sciences, University of Torino, Via L. Da Vinci 44, 10095, Grugliasco, TO, Italy
| | - Liliana Di Stasio
- Department of Agricultural, Forest and Food Sciences, University of Torino, Via L. Da Vinci 44, 10095, Grugliasco, TO, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), viale dell'Università 16, 35020, Legnaro, PD, Italy
| |
Collapse
|
13
|
Martinez-Castillero M, Toledo-Alvarado H, Pegolo S, Vazquez AI, de Los Campos G, Varona L, Finocchiaro R, Bittante G, Cecchinato A. Genetic parameters for fertility traits assessed in herds divergent in milk energy output in Holstein-Friesian, Brown Swiss, and Simmental cattle. J Dairy Sci 2020; 103:11545-11558. [PMID: 33222858 DOI: 10.3168/jds.2020-18934] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022]
Abstract
In this study, we aimed to investigate differences in the genetics of fertility traits (heritability of traits and correlations between traits in divergent environments) in dairy cows of different production levels defined on the basis of the herd-average daily milk energy output (herd-dMEO). Data were obtained from Holstein-Friesian (n = 37,359 for fertility traits, 381,334 for dMEO), Brown Swiss (n = 79,638 for fertility traits, 665,697 for dMEO), and Simmental cows (n = 63,048 for fertility traits, 448,445 for dMEO) reared in northeastern Italy. Fertility traits under study were interval from calving to first service, interval from first service to conception, days open, calving interval, calving rate, and nonreturn rate at d 56. We classified herds into low and high productivity based on the herd-average dMEO (inferred using mixed effects models). We estimated genetic parameters using Bayesian bivariate animal models, where expressions of a phenotype in the low and high dMEO herds were taken as being different-albeit correlated-traits. Fertility traits were more favorable in Simmental than in Holstein-Friesian cows, whereas for all traits, Holstein-Friesian had the highest estimates of intraherd heritability [ranging from 0.021 (0.006-0.038) to 0.126 (0.10-0.15)] and Simmental the lowest [ranging from 0.008 (0.001-0.017) to 0.101 (0.08-0.12)]. The genetic correlations between fertility traits and dMEO were moderate and unfavorable, ranging, in absolute values, from 0.527 (0.37-0.68) to 0.619 (0.50-0.73) in Holstein-Friesian; from 0.339 (0.20-0.47) to 0.556 (0.45-0.66) in Brown Swiss; and from 0.340 (0.10-0.60) to 0.475 (0.33-0.61) in Simmental cattle. The only exception was the nonreturn rate at d 56, which had weak genetic correlations with dMEO in all 3 breeds. The herd correlations between fertility and dMEO tended to be modest and favorable and the residual correlations modest and variable. The heritability of fertility traits tended to be greater in the low dMEO than in the high dMEO herds in the case of the Holstein-Friesians, but not in the case of the Brown Swiss or Simmentals. The additive genetic correlations between fertility traits in the low and high dMEO herds were always lower than 1 [0.329 (-0.17 to 0.85) to 0.934 (0.86 to 0.99)] for all traits considered in all breeds. The correlation was particularly low for the threshold characters and the interval from first service to conception in Holstein-Friesian, suggesting that the relative performances of genotypes vary significantly between herds of different dMEO levels. Although there was large variability in the estimates, results might support making separate genetic evaluations of fertility in the different herd production groups. Our results also indicate that Simmental, a dual-purpose breed, has higher fertility and lower environmental sensitivity than Holstein-Friesian, with Brown Swiss being intermediate.
Collapse
Affiliation(s)
- M Martinez-Castillero
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy
| | - H Toledo-Alvarado
- Department of Genetics and Biostatistics, School of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Ciudad Universitaria, 0451, Mexico City, México
| | - S Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy.
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Drive, East Lansing 48824
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Drive, East Lansing 48824; Department of Statistics and Probability, Michigan State University, 619 Red Cellar Road, East Lansing 48824
| | - L Varona
- Unidad de Genética Cuantitativa y Mejora Animal, Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, Calle de Miguel Servet, 177, 50013, Zaragoza, Zaragoza, Spain
| | - R Finocchiaro
- Associazione Nazionale Allevatori bovini della razza Frisona e Jersey Italiana (ANAFIJ), Via Bergamo 292, 26100 Cremona, Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy
| |
Collapse
|
14
|
Amalfitano N, Stocco G, Maurmayr A, Pegolo S, Cecchinato A, Bittante G. Quantitative and qualitative detailed milk protein profiles of 6 cattle breeds: Sources of variation and contribution of protein genetic variants. J Dairy Sci 2020; 103:11190-11208. [PMID: 33069399 DOI: 10.3168/jds.2020-18497] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/03/2020] [Indexed: 01/19/2023]
Abstract
Different fractions of milk nitrogenous compounds (not only caseins) have different effects on the nutritional value of milk, its coagulation and curd firming properties, and its cheese-making efficiency. To assess different sources of variation, especially the cows' breed and genetic variants of the main protein fractions, milk samples were collected from 1,504 cows belonging to 3 dairy breeds (Holstein-Friesian, Brown Swiss, and Jersey) and 3 dual-purpose breeds (Simmental, Rendena, and Alpine Grey) reared in 41 multibreed herds. Beyond crude protein, casein (CN), and urea, 7 protein fractions were analyzed using HPLC, and 5 other N fraction traits were calculated. All 15 traits were measured qualitatively (% of milk N) and quantitatively (g/L of milk). The HPLC technique allowed us to discriminate between the main genetic variants of β-CN, κ-CN, and β-lactoglobulin and thus to genotype the cows for the CSN2, CSN3, and BLG genes, respectively. Data were analyzed using 2 mixed models, both including the effects of herd-date, breed, parity, and lactation stage, and only one also including the effects of the genotypes of the milk proteins. Breed of cow explained 2 to 36% of phenotypic variability for all the N fractions, with the exception of the urea and total casein contents of milk and the urea and β-CN proportions of total milk N. Lactation stage had a considerable influence on the amount (g/L) of almost all the protein fractions in milk, but neither the nonprotein N fractions nor the percentage of milk N protein profile were affected. The inclusion of the CSN2, CSN3, and BLG genotypes in the model explained a large part of the total variability in all the milk protein and nonprotein fractions except urea. It also reduced the variance explained by breed and residual factors. An exception was shown by the proportion of αS1-CN variance explained by breed that moved from 13 to 28%. Similarly, for amount (g/L) of β-CN, the effect of breed became significant (12%), whereas it was almost null before inclusion of genotypes. In terms of percentage of milk N, the genotypes of CSN3 notably affected all the casein fractions, whereas the BLG genotypes had a much greater influence on most noncasein traits. The genotypes of the CSN2 gene exerted an appreciable effect on αS2-CN and not β-CN, as expected. Comparing the 2 models, we were also able to discriminate the effect of the breed on a milk N fraction, both quantitatively and qualitatively, in 2 quotas: the first due to the milk protein polymorphisms (major genes) and the second due to other genetic factors (polygene), after correcting for the effect of herd-date of sampling, parity, and lactation stage. The knowledge about the detailed milk protein profile of different cattle breeds provided by this study could be of great benefit for the dairy industry, providing new tools for the enhancement of milk payment systems and breeding program designs.
Collapse
Affiliation(s)
- Nicolò Amalfitano
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Giorgia Stocco
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Alice Maurmayr
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| |
Collapse
|
15
|
Cecchinato A, Toledo-Alvarado H, Pegolo S, Rossoni A, Santus E, Maltecca C, Bittante G, Tiezzi F. Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations. Front Genet 2020; 11:563393. [PMID: 33133149 PMCID: PMC7550782 DOI: 10.3389/fgene.2020.563393] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 08/31/2020] [Indexed: 11/13/2022] Open
Abstract
The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull’s progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (ra) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.
Collapse
Affiliation(s)
- Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padua, Italy
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, National Autonomous University of Mexico, Mexico City, Mexico
| | - Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padua, Italy
| | | | - Enrico Santus
- Italian Brown Breeders Association, Bussolengo, Italy
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC, United States
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padua, Italy
| | - Francesco Tiezzi
- Department of Animal Science, North Carolina State University, Raleigh, NC, United States
| |
Collapse
|
16
|
Bittante G, Cipolat-Gotet C, Cecchinato A. Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle. Animals (Basel) 2020; 10:ani10091654. [PMID: 32942618 PMCID: PMC7552146 DOI: 10.3390/ani10091654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/06/2020] [Accepted: 09/11/2020] [Indexed: 01/20/2023] Open
Abstract
This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH4/DM: 0.232-0.317) and methane intensity per kg of corrected milk (CH4/CM: 0.177-0.279), smaller per kg cheese solids (CH4/SO: 0.093-0.165), but greater per kg fresh cheese (CH4/CU: 0.203-0.267) and for methane production (dCH4: 0.195-0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH4/DM (0.45-0.60), and very variable for dCH4 according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH4/CM, CH4/CU and dCH4, could be considered in breeding programs aimed at the improvement of milk ecological footprint.
Collapse
Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 1, 35020 Legnaro, Italy;
| | - Claudio Cipolat-Gotet
- Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy;
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 1, 35020 Legnaro, Italy;
- Correspondence:
| |
Collapse
|
17
|
Tiplady KM, Lopdell TJ, Littlejohn MD, Garrick DJ. The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle. J Anim Sci Biotechnol 2020; 11:39. [PMID: 32322393 PMCID: PMC7164258 DOI: 10.1186/s40104-020-00445-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/09/2020] [Indexed: 11/22/2022] Open
Abstract
Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.
Collapse
Affiliation(s)
- K M Tiplady
- 1Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand.,2School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - T J Lopdell
- 1Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - M D Littlejohn
- 1Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand.,2School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - D J Garrick
- 2School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| |
Collapse
|
18
|
Saha S, Amalfitano N, Bittante G, Gallo L. Milk coagulation traits and cheese yields of purebred Holsteins and 4 generations of 3-breed rotational crossbred cows from Viking Red, Montbéliarde, and Holstein bulls. J Dairy Sci 2020; 103:3349-3362. [DOI: 10.3168/jds.2019-17576] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 11/27/2019] [Indexed: 01/18/2023]
|
19
|
Macedo Mota LF, Pegolo S, Bisutti V, Bittante G, Cecchinato A. Genomic Analysis of Milk Protein Fractions in Brown Swiss Cattle. Animals (Basel) 2020; 10:ani10020336. [PMID: 32093277 PMCID: PMC7070934 DOI: 10.3390/ani10020336] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Milk protein fractions are hugely important in the dairy industry because of the key role they play in milk technological properties. The selection of cows for milk protein fractions may, therefore, improve both the nutritional and technological characteristics of milk, and, consequently, the processing efficiency and value of the dairy product. This study estimated the genetic parameters of the major milk protein fractions (four caseins, and two whey proteins) determined variously as: (i) milk content (g/100g milk), (ii) percentage of milk nitrogen (%N) and (iii) daily yield (g/d) in Brown Swiss dairy cattle. The results showed that the (co)variances and genetic parameter estimates differed according to how the proteins were measured. These results provide useful information for developing selection strategies in dairy cattle breeding programs aimed at improving both the nutritional and technological properties of milk. Abstract Depending on whether milk protein fractions are evaluated qualitatively or quantitatively, different genetic outcomes may emerge. In this study, we compared the genetic parameters for the major milk protein fractions—caseins (αS1-, αS2-, β-, and к-CN), and whey proteins (β-lactoglobulin, β-LG; α-lactalbumin, α-LA)—estimated using the multi-trait genomic best linear unbiased prediction method and expressed variously as milk content (g/100g milk), percentage of milk nitrogen (%N) and daily yield per cow (g/d). The results showed that the genetic parameter estimates varied according to how the milk protein fractions were expressed. Heritability estimates for the caseins and whey protein fractions expressed as daily yields were lower than when they were expressed as proportions and contents, revealing important differences in genetic outcomes. The proportion and the content of β-CN were negatively correlated with the proportions and contents of αS1-CN, αS2-CN, and к-CN, while the daily yield of β–CN was negatively correlated with the daily yields of αS1-CN and αS2-CN. The Spearman’s rank correlations and the coincidence rates between the various predicted genomic breeding values (GEBV) for the milk protein fractions expressed in different ways indicated that these differences had a significant effect on the ranking of the animals. The results suggest that the way milk protein fractions are expressed has implications for breeding programs aimed at improving milk nutritional and technological characteristics.
Collapse
|
20
|
Stocco G, Summer A, Malacarne M, Cecchinato A, Bittante G. Detailed macro- and micromineral profile of milk: Effects of herd productivity, parity, and stage of lactation of cows of 6 dairy and dual-purpose breeds. J Dairy Sci 2019; 102:9727-9739. [PMID: 31477292 DOI: 10.3168/jds.2019-16834] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Accepted: 07/11/2019] [Indexed: 01/02/2023]
Abstract
The aim of this study was to quantify the major sources of variation in the levels of 15 minerals in individual milk samples collected from cows raised in multibreed dairy herds. The herds (n = 27) were classified into 2 categories, according to milk productivity. Milk productivity was based on the net energy of lactating cows' average daily milk yield. Milk samples were collected from 240 cows of 6 different breeds: 3 specialized dairy (Holstein-Friesian, Brown Swiss, and Jersey) and 3 dual-purpose (Simmental, Rendena, and Alpine Grey). The samples were analyzed for macro-elements (Na, Mg, P, S, K, and Ca), essential micro-elements (Mn, Fe, Cu, Zn, and Se), and environmental micro-elements (B, Si, Sr, and Sn), using inductively coupled plasma-optical emission spectrometry. Data were analyzed using a linear mixed model that included fixed effects of days in milk (DIM), parity, breed, and herd productivity, and a random effect of herd-date within productivity level. Results showed that the effect of herd-date varied across minerals. It was especially large for environmental minerals (ranging from 47 to 91% of total variance) and ranged from 11 to 61% for macrominerals and essential microminerals. Milk samples collected from farms with a high level of herd productivity had a richer mineral profile than samples from low-productivity herds. Parity only influenced macrominerals, with the exception of S and Ca, while DIM influenced almost all minerals, with a few exceptions among the environmental elements. Differences in mineral profile were small between specialized and dual-purpose breeds, but they were large within the group of the specialized cows. These breed differences were reduced after adjusting for milk quality and yield, particularly in the case of milk Mg, S, Ca, Mn, and Zn levels. Milk samples from the Jersey and Brown Swiss cows had higher mineral levels (Sn excluded) than milk from the Holstein-Friesian cows; the other breeds of Alpine origin produced milk of intermediate quality. Our findings suggest that breed has a stronger effect on macrominerals and some of the essential microminerals than herd productivity, parity, and DIM. The modification of the mineral profile in milk seems possible for many minerals, but it likely depends on genetics (e.g., breed, selection) and on environmental and management factors in variable proportions according to the mineral considered.
Collapse
Affiliation(s)
- G Stocco
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, viale dell'Università 16, 35020 Legnaro (PD), Italy; Department of Veterinary Science, University of Parma, via del Taglio 10, 43126 Parma, Italy
| | - A Summer
- Department of Veterinary Science, University of Parma, via del Taglio 10, 43126 Parma, Italy
| | - M Malacarne
- Department of Veterinary Science, University of Parma, via del Taglio 10, 43126 Parma, Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, viale dell'Università 16, 35020 Legnaro (PD), Italy
| |
Collapse
|
21
|
Lei T, Lin XH, Sun DW. Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA–LDA models built by hyperspectral data. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00234-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|
22
|
Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.04.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
23
|
Tiplady KM, Sherlock RG, Littlejohn MD, Pryce JE, Davis SR, Garrick DJ, Spelman RJ, Harris BL. Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle. J Dairy Sci 2019; 102:6357-6372. [PMID: 31030929 DOI: 10.3168/jds.2018-16144] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 03/04/2019] [Indexed: 01/02/2023]
Abstract
The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.
Collapse
Affiliation(s)
- K M Tiplady
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand; School of Agriculture, Massey University, Ruakura, Hamilton 3240, New Zealand.
| | - R G Sherlock
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
| | - M D Littlejohn
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand; School of Agriculture, Massey University, Ruakura, Hamilton 3240, New Zealand
| | - J E Pryce
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - S R Davis
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
| | - D J Garrick
- School of Agriculture, Massey University, Ruakura, Hamilton 3240, New Zealand
| | - R J Spelman
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
| | - B L Harris
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
| |
Collapse
|
24
|
Saha S, Gallo L, Bittante G, Schiavon S, Bergamaschi M, Gianesella M, Fiore E. A Study on the Effects of Rumen Acidity on Rumination Time and Yield, Composition, and Technological Properties of Milk from Early Lactating Holstein Cows. Animals (Basel) 2019; 9:ani9020066. [PMID: 30795570 PMCID: PMC6406462 DOI: 10.3390/ani9020066] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 02/16/2019] [Indexed: 11/30/2022] Open
Abstract
Simple Summary The increase in milk yield achieved in recent decades by the dairy sector has been sustained by feeding dairy cows with more concentrates and less forage. This leads to increasing rumen acidity, a status widespread in high-producing dairy cows that may affect feed intake, impair ruminal digestion, and cause diarrhea, laminitis, inflammation, and liver abscesses. The effects of rumen acidity on milk yield and composition are controversial, while those on milk coagulation properties and cheese yield have not yet been explored. This study investigated whether the rumen acidity status affects rumination time, and the production, composition, coagulation properties and cheese yield of milk obtained by 100 early-lactating Holstein cows. The variation in rumen acidity was associated with changes in the cows’ rumen fluid composition and circadian pattern of rumination time. Moreover, daily milk yield linearly decreased as the rumen acidity increased. Conversely, the composition and technological properties of milk were unaffected, even when there were differences in rumen acidity, suggesting that variation in rumen acidity has little impact on cheese-making traits. Abstract The use of high grain rations in dairy cows is related to an increase in rumen acidity. This study investigated whether the rumen acidity status affects rumination time (RT), and the production, composition, coagulation properties (MCPs) and cheese yield (CY) of milk. One hundred early-lactating Holstein cows with no clinical signs of disease and fed total mixed rations were used. Rumen fluid was collected once from each cow by rumenocentesis to determine pH and volatile fatty acid (VFA) content. The cows were classified according to the quartile of rumen acidity (QRA), a factor defined by multivariate analysis and associated with VFA and pH. Rumen fluid pH averaged 5.61 in the first quartile and 6.42 in the fourth, and total VFA content increased linearly with increasing rumen acidity. In addition, RT increased as rumen acidity increased, but only in the daily time interval from 08:00 to 12:00. Milk yield linearly decreased as rumen acidity increased, whereas QRA did not affect pH, fat or protein contents of milk. Furthermore, the MCPs, assessed by lactodynamograph, and CY were unaffected by QRA. It is suggested that differences in rumen acidity have little influence on the nutrient content, coagulation properties and CY of milk.
Collapse
Affiliation(s)
- Sudeb Saha
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Luigi Gallo
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Matteo Bergamaschi
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Matteo Gianesella
- Department of Animal Medicine, Production and Health, University of Padova Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - Enrico Fiore
- Department of Animal Medicine, Production and Health, University of Padova Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| |
Collapse
|
25
|
Rovere G, de Los Campos G, Tempelman RJ, Vazquez AI, Miglior F, Schenkel F, Cecchinato A, Bittante G, Toledo-Alvarado H, Fleming A. A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows. J Dairy Sci 2018; 102:1354-1363. [PMID: 30580946 DOI: 10.3168/jds.2018-15109] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/28/2018] [Indexed: 11/19/2022]
Abstract
Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.
Collapse
Affiliation(s)
- G Rovere
- Department of Animal Science, Michigan State University, East Lansing 48824; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824.
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824; Department of Statistics and Probability, Michigan State University, East Lansing 48824
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824
| | - F Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Canadian Dairy Network, Guelph, Ontario, Canada N1K 1E5
| | - F Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy
| | - H Toledo-Alvarado
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy
| | - A Fleming
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Canadian Dairy Network, Guelph, Ontario, Canada N1K 1E5
| |
Collapse
|
26
|
Pegolo S, Bergamaschi M, Gasperi F, Biasioli F, Cecchinato A, Bittante G. Integrated PTR-ToF-MS, GWAS and biological pathway analyses reveal the contribution of cow's genome to cheese volatilome. Sci Rep 2018; 8:17002. [PMID: 30451907 PMCID: PMC6242841 DOI: 10.1038/s41598-018-35323-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 11/01/2018] [Indexed: 11/21/2022] Open
Abstract
Volatile organic compounds (VOCs) are small molecules that contribute to the distinctive flavour of cheese which is an important attribute for consumer acceptability. To investigate whether cow's genetic background might contribute to cheese volatilome, we carried out genome-wide association studies (GWAS) and pathway-based analyses for 173 spectrometric peaks tentatively associated with several VOCs obtained from proton-transfer-reaction mass spectrometry (PTR-ToF-MS) analyses of 1,075 model cheeses produced using raw whole-milk from Brown Swiss cows. Overall, we detected 186 SNPs associated with 120 traits, several of which mapped close to genes involved in protein (e.g. CSN3, GNRHR and FAM169A), fat (e.g. AGPAT3, SCD5, and GPAM) and carbohydrate (e.g. B3GNT2, B4GALT1, and PHKB) metabolism. Gene set enrichment analysis showed that pathways connected with proteolysis/amino acid metabolism (purine and nitrogen metabolism) as well as fat metabolism (long-term potentiation) and mammary gland function (tight junction) were overrepresented. Our results provide the first evidence of a putative link between cow's genes and cheese flavour and offer new insights into the role of potential candidate loci and the biological functions contributing to the cheese volatilome.
Collapse
Affiliation(s)
- Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy.
| | - Matteo Bergamaschi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
| | - Flavia Gasperi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010, San Michele all'Adige, TN, Italy
| | - Franco Biasioli
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010, San Michele all'Adige, TN, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, Padua, Italy
| |
Collapse
|
27
|
Sanchez M, El Jabri M, Minéry S, Wolf V, Beuvier E, Laithier C, Delacroix-Buchet A, Brochard M, Boichard D. Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large data set of Montbéliarde cows. J Dairy Sci 2018; 101:10048-10061. [DOI: 10.3168/jds.2018-14878] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 07/13/2018] [Indexed: 11/19/2022]
|
28
|
Toledo-Alvarado H, Vazquez AI, de los Campos G, Tempelman RJ, Gabai G, Cecchinato A, Bittante G. Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows. J Dairy Sci 2018; 101:9135-9153. [DOI: 10.3168/jds.2018-14480] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/31/2018] [Indexed: 11/19/2022]
|
29
|
Integration of GWAS, pathway and network analyses reveals novel mechanistic insights into the synthesis of milk proteins in dairy cows. Sci Rep 2018; 8:566. [PMID: 29330500 PMCID: PMC5766549 DOI: 10.1038/s41598-017-18916-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 12/18/2017] [Indexed: 01/30/2023] Open
Abstract
The quantities and proportions of protein fractions have notable effects on the nutritional and technological value of milk. Although much is known about the effects of genetic variants on milk proteins, the complex relationships among the set of genes and pathways regulating the different protein fractions synthesis and secretion into milk in dairy cows are still not completely understood. We conducted genome-wide association studies (GWAS) for milk nitrogen fractions in a cohort of 1,011 Brown Swiss cows, which uncovered 170 significant single nucleotide polymorphism (SNPs), mostly located on BTA6 and BTA11. Gene-set analysis and the network-based Associated Weight Matrix approach revealed that the milk proteins associated genes were involved in several biological functions, particularly ion and cation transmembrane transporter activity and neuronal and hormone signalling, according to the structure and function of casein micelles. Deeper analysis of the transcription factors and their predicted target genes within the network revealed that GFI1B, ZNF407 and NR5A1 might act as master regulators of milk protein synthesis and secretion. The information acquired provides novel insight into the regulatory mechanisms controlling milk protein synthesis and secretion in bovine mammary gland and may be useful in breeding programmes aimed at improving milk nutritional and/or technological properties.
Collapse
|
30
|
Breed of cow and herd productivity affect milk nutrient recovery in curd, and cheese yield, efficiency and daily production. Animal 2018; 12:434-444. [DOI: 10.1017/s1751731117001471] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
|
31
|
Tagliapietra F, Simonetto A, Schiavon S. Growth performance, carcase characteristics and meat quality of crossbred bulls and heifers from double-muscled Belgian Blue sires and Brown Swiss, Simmental and Rendena dams. ITALIAN JOURNAL OF ANIMAL SCIENCE 2017. [DOI: 10.1080/1828051x.2017.1401911] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Franco Tagliapietra
- Dipartimento di Agronomia Animali Alimenti Risorse Naturali e Ambiente (DAFNAE), University of Padova, Legnaro, Padova, Italy
| | - Alberto Simonetto
- Dipartimento di Agronomia Animali Alimenti Risorse Naturali e Ambiente (DAFNAE), University of Padova, Legnaro, Padova, Italy
| | - Stefano Schiavon
- Dipartimento di Agronomia Animali Alimenti Risorse Naturali e Ambiente (DAFNAE), University of Padova, Legnaro, Padova, Italy
| |
Collapse
|
32
|
Bittante G, Cecchinato A, Tagliapietra F, Verdiglione R, Simonetto A, Schiavon S. Crossbred young bulls and heifers sired by double-muscled Piemontese or Belgian Blue bulls exhibit different effects of sexual dimorphism on fattening performance and muscularity but not on meat quality traits. Meat Sci 2017; 137:24-33. [PMID: 29149626 DOI: 10.1016/j.meatsci.2017.11.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/30/2017] [Accepted: 11/02/2017] [Indexed: 01/06/2023]
Abstract
As double-muscled Belgian Blue (BB) and Piemontese (PIEM) breeds are heavily selected for different objectives (the former mainly for muscularity, the latter for ease of calving), the aim of this study was to compare sexual dimorphism in 56 crossbred young bulls and heifers obtained from dairy cows mated to bulls of the two beef breeds. Young PIEM- and BB-sired bulls had similar fattening performances and beef traits, although the BB crossbreds were slightly more muscular. Otherwise, the BB-sired heifers exceeded the PIEM-sired heifers in growth rate (1.12 vs. 0.98kg/d), feed efficiency (0.129 vs. 0.121kg/kg DM), increases in muscle scores (1.45 vs. 0.98 SEUROP scores) and carcass yield (0.612 vs. 0.605), but not in fatness, retail cut proportions and meat quality traits. Sexual dimorphism is, therefore, less distinct in BB than in PIEM crossbreds. In conclusion, BB sires are to be preferred for "product quality", and PIEM sires for "process quality", on account of welfare and ethical issues.
Collapse
Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020 Legnaro, PD, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020 Legnaro, PD, Italy
| | - Franco Tagliapietra
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020 Legnaro, PD, Italy.
| | - Rina Verdiglione
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020 Legnaro, PD, Italy
| | - Alberto Simonetto
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020 Legnaro, PD, Italy
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020 Legnaro, PD, Italy
| |
Collapse
|
33
|
Variation in blood serum proteins and association with somatic cell count in dairy cattle from multi-breed herds. Animal 2017; 11:2309-2319. [PMID: 28560948 DOI: 10.1017/s1751731117001227] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Blood serum proteins are significant indicators of animal health. Nevertheless, several factors should be considered to appropriately interpret their concentrations in blood. Therefore, the objectives of this study were (1) to assess the effect of herd productivity, breed, age and stage of lactation on serum proteins and (2) to investigate association between serum proteins and somatic cell count (SCC) in dairy cattle. Milk and blood samples were collected from 1508 cows of six different breeds (Holstein Friesian, Brown Swiss, Jersey, Simmental, Rendena and Alpine Grey) that were housed in 41 multi-breed herds. Milk samples were analyzed for composition and SCC, while blood samples were analyzed for serum proteins (i.e. total protein, albumin, globulin and albumin-to-globulin ratio (A : G)). Herds were classified as low or high production, according to the cow's average daily milk energy yield adjusted for breed, days in milk (DIM) and parity. Data were analyzed using a linear mixed model that included the fixed effects of DIM, parity, SCS, breed, herd productivity and the random effect of the Herd-test date within productivity level. Cows in high producing herds (characterized also by greater use of concentrates in the diet) had greater serum albumin concentrations. Breed differences were reported for all traits, highlighting a possible genetic mechanism. The specialized breed Jersey and the two dual-purpose local breeds (Alpine Grey and Rendena) had the lowest globulin concentration and greatest A : G. Changes in serum proteins were observed through lactation. Total protein reached the highest concentration during the 4th month of lactation. Blood albumin increased with DIM following a quadratic pattern, while globulin decreased linearly. As a consequence, A : G increased linearly during lactation. Older cows had greater total protein and globulin concentrations, while albumin concentration seemed to be not particularly affected by age. A linear relationship between serum proteins and SCS was observed. High milk SCS was associated with greater total protein and globulin concentrations in blood. The rise in globulin concentration, together with a decrease in albumin concentrations, resulted in a decline in A : G as SCS of milk increased. In conclusion, such non-genetic factors must be considered to appropriately interpret serum proteins as potential animal welfare indicator and their evaluation represents an important first-step for future analysis based on the integration of metabolomics, genetic and genomic information for improving the robustness of dairy cows.
Collapse
|
34
|
Choi H, Kim SJ, Lee SY, Choi MJ. Effect of Abalone Hydrolysates Encapsulated by Double Emulsion on the Physicochemical and Sensorial Properties of Fresh Cheese. Korean J Food Sci Anim Resour 2017; 37:210-218. [PMID: 28515645 PMCID: PMC5434208 DOI: 10.5851/kosfa.2017.37.2.210] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 02/16/2017] [Accepted: 02/17/2017] [Indexed: 11/06/2022] Open
Abstract
The intake of dietary salt through food now exceeds current nutritional recommendations and is thought to have negative effects on human health, such as the increasing prevalence of hypertension. This study was performed to investigate whether W1/O/W2 double emulsions can be used to enhance the saltiness of cheese without increasing the salt content (W1 is distilled water or 1% abalone hydrolysate, and W2 is 1% NaCl or 1% abalone hydrolysate + 1% NaCl solution). We also investigated the effect of adding abalone hydrolysate to the double emulsion as a saltiness enhancer. The cheeses were physico-chemically evaluated to determine curd yield, pH value, moisture content, color, texture, salt release rate, and sensory properties. No significant differences were observed in curd yield, pH value, moisture content, lightness, or redness between the cheeses made with and without the double emulsion. However, in the evaluation of salt release rate, fresh cheese made with double emulsion (W1 = distilled water, W2 = 1% NaCl + 1% abalone hydrolysate) was detected earlier than the control or the other treatments. In the sensory evaluation, fresh cheese made with the double emulsion showed higher scores for saltiness and overall preference than the control or the other treatments. We concluded that abalone hydrolysate encapsulated in a double emulsion (W1 is water and W2 is abalone hydrolysate and NaCl solution) could enhance the saltiness of fresh cheese while maintaining the same salt concentration, without altering its physical properties.
Collapse
Affiliation(s)
- HeeJeong Choi
- Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea
| | - Soo-Jin Kim
- Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea
| | - Sang-Yoon Lee
- Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea
| | - Mi-Jung Choi
- Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea
| |
Collapse
|
35
|
Cziszter LT, Ilie DE, Neamt RI, Neciu FC, Saplacan SI, Gavojdian D. Comparative study on production, reproduction and functional traits between Fleckvieh and Braunvieh cattle. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2016; 30:666-671. [PMID: 27857028 PMCID: PMC5411826 DOI: 10.5713/ajas.16.0588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 08/30/2016] [Accepted: 11/14/2016] [Indexed: 12/04/2022]
Abstract
Objective Aim of the current comparative study was to evaluate production outputs, reproduction efficiency and functional traits in dual-purpose Fleckvieh and Braunvieh cows, reared under temperate European conditions. Methods A data-set from 414 Fleckvieh and 42 Braunvieh cows and 799 lactations was analysed. ID tag number, milk yield per milking session, number of steps/interval and milk conductivity were recorded and collected daily using AfiMilk 3.076 A-DU software (Afimilk Ltd., Kibbutz, Israel). Production and milk quality data were taken from the results of the official performance recordings and the reproductive outputs of cows were recorded by the research stations veterinarians. Comparisons between the two genotypes were carried out using the one way analysis of variance protocol, with categorical factor being considered the breed of cows. All the statistical inferences were carried out using Statistica software (StatSoft Inc., Tulsa, OK, USA). Results Fleckvieh cows significantly outperformed (p≤0.05) the Braunvieh herd, with average milk yields of 5,252.1±35.79 kg and 4,897.6±128.94 kg, respectively. Age at first calving was significantly (p≤0.01) influenced by the breed, with Fleckvieh heifers being more precocious (32.8±0.29 mo) compared to those of Braunvieh breed (35.7±0.84 mo). Reproduction efficiency as defined by the number of inseminations per gestation, calving interval, dystocia, days dry and days open, was not influenced by genotype (p>0.05). Incidences of sub-clinical mastitis, clinical mastitis, lameness and abortions were not influenced by the breed factor (p>0.05). Stayability of cows was significantly (p≤0.001) influenced by genotype, with Braunvieh cows having an average age at culling of 117.88±11.78 months compared to 90.88±2.89 months in Fleckvieh. Conclusion Overall, results have shown that genotype significantly influenced milk yield, age at first calving and longevity.
Collapse
Affiliation(s)
- Ludovic-Toma Cziszter
- Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad 310059, Romania.,Banat's University of Agricultural Sciences and Veterinary Medicine 'King Michael I' from Timişoara, Timisoara 300645, Romania
| | - Daniela-Elena Ilie
- Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad 310059, Romania
| | - Radu-Ionel Neamt
- Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad 310059, Romania
| | - Florin-Cristian Neciu
- Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad 310059, Romania
| | - Silviu-Ilie Saplacan
- Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad 310059, Romania
| | - Dinu Gavojdian
- Research and Development Station for Bovine Arad, Academy for Agricultural and Forestry Sciences, Arad 310059, Romania
| |
Collapse
|
36
|
Bergamaschi M, Cecchinato A, Biasioli F, Gasperi F, Martin B, Bittante G. From cow to cheese: genetic parameters of the flavour fingerprint of cheese investigated by direct-injection mass spectrometry (PTR-ToF-MS). Genet Sel Evol 2016; 48:89. [PMID: 27852216 PMCID: PMC5112698 DOI: 10.1186/s12711-016-0263-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 11/02/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Volatile organic compounds determine important quality traits in cheese. The aim of this work was to infer genetic parameters of the profile of volatile compounds in cheese as revealed by direct-injection mass spectrometry of the headspace gas from model cheeses that were produced from milk samples from individual cows. METHODS A total of 1075 model cheeses were produced using raw whole-milk samples that were collected from individual Brown Swiss cows. Single spectrometry peaks and a combination of these peaks obtained by principal component analysis (PCA) were analysed. Using a Bayesian approach, we estimated genetic parameters for 240 individual spectrometry peaks and for the first ten principal components (PC) extracted from them. RESULTS Our results show that there is some genetic variability in the volatile compound fingerprint of these model cheeses. Most peaks were characterized by a substantial heritability and for about one quarter of the peaks, heritability (up to 21.6%) was higher than that of the best PC. Intra-herd heritability of the PC ranged from 3.6 to 10.2% and was similar to heritabilities estimated for milk fat, specific fatty acids, somatic cell count and some coagulation parameters in the same population. We also calculated phenotypic correlations between PC (around zero as expected), the corresponding genetic correlations (from -0.79 to 0.86) and correlations between herds and sampling-processing dates (from -0.88 to 0.66), which confirmed that there is a relationship between cheese flavour and the dairy system in which cows are reared. CONCLUSIONS This work reveals the existence of a link between the cow's genetic background and the profile of volatile compounds in cheese. Analysis of the relationships between the volatile organic compound (VOC) content and the sensory characteristics of cheese as perceived by the consumer, and of the genetic basis of these relationships could generate new knowledge that would open up the possibility of controlling and improving the sensory properties of cheese through genetic selection of cows. More detailed investigations are necessary to connect VOC with the sensory properties of cheese and gain a better understanding of the significance of these new phenotypes.
Collapse
Affiliation(s)
- Matteo Bergamaschi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, PD, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, PD, Italy.
| | - Franco Biasioli
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010, San Michele all'Adige, TN, Italy
| | - Flavia Gasperi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via E. Mach 1, 38010, San Michele all'Adige, TN, Italy
| | - Bruno Martin
- INRA, UMR Herbivores, 63122, Saint-Genès Champanelle, France.,Clermont Université, VetAgro Sup, BP 10448, 63000, Clermont-Ferrand, France
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padua, Viale dell'Università 16, 35020, Legnaro, PD, Italy
| |
Collapse
|
37
|
Stocco G, Cipolat-Gotet C, Bobbo T, Cecchinato A, Bittante G. Breed of cow and herd productivity affect milk composition and modeling of coagulation, curd firming, and syneresis. J Dairy Sci 2016; 100:129-145. [PMID: 27837976 DOI: 10.3168/jds.2016-11662] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 09/17/2016] [Indexed: 12/12/2022]
Abstract
Milk coagulation properties (MCP) have been widely investigated in the past using milk collected from different cattle breeds and herds. However, to our knowledge, no previous studies have assessed MCP in individual milk samples from several multi-breed herds characterized by either high or low milk productivity, thereby allowing the effects of herd and cow breed to be evaluated independently. Multi-breed herds (n=41) were classified into 2 categories based on milk productivity (high vs. low), defined according to the average milk net energy yielded daily by lactating cows. Milk samples were taken from 1,508 cows of 6 different breeds: 3 specialized dairy (Holstein-Friesian, Brown Swiss, Jersey) and 3 dual-purpose (Simmental, Rendena, Alpine Grey) breeds, and analyzed in duplicate (3,016 tests) using 2 lactodynamographs to obtain 240 curd firming (CF) measurements over 60min (1 every 15 s) for each duplicate. The 5 traditional single-point MCP (RCT, k20, a30, a45, and a60) were yielded directly by the instrument from the available CF measures. All 240 CF measures of each replicate were also used to estimate 4 individual equation parameters: RCT estimated according to curd firm change over time modeling (RCTeq), asymptotic potential curd firmness (CFP), curd firming instant rate constant (kCF), and syneresis instant rate constant (kSR) and 2 derived traits: maximum curd firmness achieved within 45min (CFmax) and time at achievement of CFmax (tmax) by curvilinear regression using a nonlinear procedure. Results showed that the effect of herd-date on traditional and modeled MCP was modest, ranging from 6.1% of total variance for k20 to 10.7% for RCT, whereas individual animal variance was the highest, ranging from 32.0% for tmax to 82.5% for RCTeq. The repeatability of MCP was high (>80%) for all traits except those associated with the last part of the lactodynamographic curve (i.e., a60, kSR, kCF, and tmax: 57 to 71%). Reproducibility, taking into account the effect of instrument, was equal to or slightly lower than repeatability. Milk samples collected in farms characterized by high productivity exhibited delayed coagulation (RCTeq: 18.6 vs. 16.3min) but greater potential curd firmness (CFP: 76.8 vs. 71.9mm) compared with milk samples collected from low-productivity herds. Parity and days in milk influenced almost all MCP. Large differences in all MCP traits were observed among breeds, both between specialized and dual-purpose breeds and within these 2 groups of breeds, even after adjusting for milk quality and yield. Milk quality and MCP of samples from Jersey cows, and coagulation time of samples from Rendena cows were better than in milk from Holstein-Friesian cows, and intermediate results were found with the other breeds of Alpine origin. The results of this study, taking into account the intrinsic limitation of this technique, show that the effects of breed on traditional and modeled MCP are much greater than the effects of herd productivity class, parity, and DIM. Moreover, the variance in individual animals is much greater than the variance in individual herds within herd productivity class. It seems that improvement in MCP depends more on genetics (e.g., breed, selection) than on environmental and management factors.
Collapse
Affiliation(s)
- G Stocco
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - C Cipolat-Gotet
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - T Bobbo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro (PD), Italy
| |
Collapse
|
38
|
Schiavon S, Cesaro G, Cecchinato A, Cipolat-Gotet C, Tagliapietra F, Bittante G. The influence of dietary nitrogen reduction and conjugated linoleic acid supply to dairy cows on fatty acids in milk and their transfer to ripened cheese. J Dairy Sci 2016; 99:8759-8778. [DOI: 10.3168/jds.2016-11371] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 07/07/2016] [Indexed: 01/28/2023]
|
39
|
Variation of milk coagulation properties, cheese yield, and nutrients recovery in curd of cows of different breeds before, during and after transhumance to highland summer pastures. J DAIRY RES 2016; 84:39-48. [DOI: 10.1017/s0022029916000583] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper aimed at evaluating the effect of summer transhumance to mountain pastures of dairy cows of different breeds on cheese-making ability of milk. Data were from 649 dairy cows of specialized (Holstein Friesian and Brown Swiss) dual purpose (Simmental) and local (mostly Rendena and Alpine Grey) breeds. The Fourier-Transform Infra-Red Spectra (FTIRS) of their milk samples were collected before and after transhumance in 109 permanent dairy farms, and during transhumance in 14 summer farms (with multi-breeds herds) of the Trento Province, north-eastern Italy. A variety of 18 traits describing milk coagulation, curd firming, cheese yield and nutrients recovery in curd/loss in whey were predicted on the basis of FTIRS collected at the individual cow level. Moving the cows to summer farms improved curd firming traits but reduced cheese yields because of an increase of water and fat lost in the whey. During summer grazing, most of cheese-making traits improved, often non-linearly. The milk from summer farms supplementing cows with more concentrates showed better curd firming and cheese yield, because of lower fat lost in the whey. The breed of cows affected almost all the traits with a worst cheese-making ability for milk samples of Holsteins through all the trial, and interacted with concentrate supplementation because increasing compound feed tended to improve cheese-making traits for all breed, with the exception of local breeds for coagulation time and of Brown Swiss for curd firming time. In general, summer transhumance caused a favourable effect on cheese-making aptitude of milk, even though with some difference according to parity, initial days in milk, breed and concentrate supplementation of cows.
Collapse
|
40
|
Bergamaschi M, Cipolat-Gotet C, Stocco G, Valorz C, Bazzoli I, Sturaro E, Ramanzin M, Bittante G. Cheesemaking in highland pastures: Milk technological properties, cream, cheese and ricotta yields, milk nutrients recovery, and products composition. J Dairy Sci 2016; 99:9631-9646. [PMID: 27665138 DOI: 10.3168/jds.2016-11199] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 08/08/2016] [Indexed: 11/19/2022]
Abstract
Summer transhumance of dairy cows to high Alpine pastures is still practiced in many mountainous areas. It is important for many permanent dairy farms because the use of highland pastures increases milk production and high-priced typical local dairy products often boost farm income. As traditional cheese- and ricotta-making procedures in Alpine pastures are central to this dairy system, the objective of this study was to characterize the quality and efficiency of products and their relationships with the quality and availability of grass during the grazing season. The milk from 148 cows from 12 permanent farms reared on a temporary farm located in Alpine pastures was processed every 2wk during the summer (7 cheesemakings from late June to early September). During each processing, 11 dairy products (4 types of milk, 2 by-products, 3 fresh products, and 2 ripened cheeses) were sampled and analyzed. In addition, 8 samples of fresh forage from the pasture used by the cows were collected and analyzed. At the beginning of the pasture season the cows were at 233±90d in milk, 2.4±1.7 parities, and produced 23.6±5.7kg/d of milk. The milk yield decreased with the move from permanent to temporary farms and during the entire summer transhumance, but partly recovered after the cows returned to the permanent farms. Similar trends were observed for the daily yields of fat, protein, casein, lactose, and energy, as we found no large variations in the quality of the milk, with the exception of the first period of Alpine pasture. The somatic cell counts of milk increased during transhumance, but this resulted from a concentration of cells in a lower quantity of milk rather than an increase in the total number of cells ejected daily from the udder. We noted a quadratic trend in availability of forage (fresh and dry matter weight per hectare), with a maximum in late July. The quality of forage also varied during the summer with a worsening of chemical composition. The evening milk (before and after natural creaming), the whole morning milk, and the mixed vat milk had different chemical compositions, traditional coagulation properties, and curd-firming modeling parameters. These variations over the pasture season were similar to the residual variations with respect to chemical composition, and much lower with respect to coagulation and curd-firming traits. Much larger variations were noted in cream, cheese, and ricotta yields, as well as in nutrient recoveries in curd during the pasture season. The protein content of forage was correlated with some of the coagulation and curd-firming traits, the ether extract of forage was positively correlated with milk fat content and cheese yields, and fiber fractions of forage were unfavorably correlated with some of the chemical and technological traits. Traditional cheese- and ricotta-making procedures showed average cream, cheese, and ricotta yields of 6.3, 14.2, and 4.9%, respectively, and an overall recovery of almost 100% of milk fat, 88% of milk protein, and 60% of total milk solids.
Collapse
Affiliation(s)
- M Bergamaschi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - C Cipolat-Gotet
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - G Stocco
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - C Valorz
- Breeders Federation of Trento Province, via delle Bettine, 40, 38100 Trento, Italy
| | - I Bazzoli
- Breeders Federation of Trento Province, via delle Bettine, 40, 38100 Trento, Italy
| | - E Sturaro
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - M Ramanzin
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| |
Collapse
|
41
|
Manca M, Serdino J, Gaspa G, Urgeghe P, Ibba I, Contu M, Fresi P, Macciotta N. Derivation of multivariate indices of milk composition, coagulation properties, and individual cheese yield in dairy sheep. J Dairy Sci 2016; 99:4547-4557. [DOI: 10.3168/jds.2015-10589] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 03/02/2016] [Indexed: 12/17/2022]
|
42
|
Dadousis C, Biffani S, Cipolat-Gotet C, Nicolazzi E, Rossoni A, Santus E, Bittante G, Cecchinato A. Genome-wide association of coagulation properties, curd firmness modeling, protein percentage, and acidity in milk from Brown Swiss cows. J Dairy Sci 2016; 99:3654-3666. [DOI: 10.3168/jds.2015-10078] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 01/20/2016] [Indexed: 11/19/2022]
|
43
|
Cecchinato A, Bittante G. Genetic and environmental relationships of different measures of individual cheese yield and curd nutrients recovery with coagulation properties of bovine milk. J Dairy Sci 2016; 99:1975-1989. [DOI: 10.3168/jds.2015-9629] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 11/11/2015] [Indexed: 11/19/2022]
|
44
|
Cipolat-Gotet C, Cecchinato A, Stocco G, Bittante G. The 9-MilCA method as a rapid, partly automated protocol for simultaneously recording milk coagulation, curd firming, syneresis, cheese yield, and curd nutrients recovery or whey loss. J Dairy Sci 2016; 99:1065-1082. [DOI: 10.3168/jds.2015-9734] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 11/02/2015] [Indexed: 11/19/2022]
|
45
|
Bergamaschi M, Biasioli F, Cappellin L, Cecchinato A, Cipolat-Gotet C, Cornu A, Gasperi F, Martin B, Bittante G. Proton transfer reaction time-of-flight mass spectrometry: A high-throughput and innovative method to study the influence of dairy system and cow characteristics on the volatile compound fingerprint of cheeses. J Dairy Sci 2015; 98:8414-27. [PMID: 26476950 DOI: 10.3168/jds.2015-9803] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 08/29/2015] [Indexed: 11/19/2022]
Abstract
The aim of this work was to study the effect of dairy system and individual cow-related factors on the volatile fingerprint of a large number of individual model cheeses analyzed by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). A total of 1,075 model cheeses were produced using milk samples collected from individual Brown Swiss cows reared in 72 herds located in mountainous areas of Trento province (Italy). The herds belonged to 5 main dairy systems ranging from traditional to modern and the cows presented different daily milk yields (24.6±7.9kg × d(-1)), stages of lactation (199±138 d in milk), and parities (2.7±1.8). The PTR-ToF-MS revealed 619 peaks, of which the 240 most intense were analyzed, and 61 of these were tentatively attributed to relevant volatile organic compounds on the basis of their fragmentation patterns and data from the literature. Principal component analysis was used to convert the multiple responses characterizing the PTR-ToF-MS spectra into 5 synthetic variables representing 62% of the total information. These principal components were related to groups of volatile compounds tentatively attributed to different peaks and used to investigate the relationship of the volatile compound profile obtained by PTR-ToF-MS to animal and farm characteristics. Lactation stage is related to 4 principal components which brought together 52.9% of the total variance and 57.9% of the area of analyzed peaks. In particular, 2 principal components were positively related to peaks tentatively attributed to aldehydes and ketones and negatively related to alcohols, esters, and acids, which displayed a linear increase during lactation. The second principal component was affected by dairy system; it was higher in the modern system in which cows received total mixed rations. The third principal component was positively related to daily milk production. In summary, we report the first application of this innovative, high-throughput technique to study the effects of dairy system and individual animal factors on volatile organic compounds of model cheeses. Individual cheesemaking procedures together with this spectrometric technique open new avenues for genetic selection of dairy species with respect to both milk and cheese quality.
Collapse
Affiliation(s)
- M Bergamaschi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy; Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) Via E. Mach 1, 38010 San Michele all'Adige (TN), Italy
| | - F Biasioli
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) Via E. Mach 1, 38010 San Michele all'Adige (TN), Italy.
| | - L Cappellin
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) Via E. Mach 1, 38010 San Michele all'Adige (TN), Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - C Cipolat-Gotet
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - A Cornu
- INRA, UMR 1213 Herbivores, F-63122 Saint-Genès Champanelle, France; Clermont Université, VetAgro Sup, BP 10448, F-63000 Clermont-Ferrand, France
| | - F Gasperi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach (FEM) Via E. Mach 1, 38010 San Michele all'Adige (TN), Italy
| | - B Martin
- INRA, UMR 1213 Herbivores, F-63122 Saint-Genès Champanelle, France; Clermont Université, VetAgro Sup, BP 10448, F-63000 Clermont-Ferrand, France
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| |
Collapse
|
46
|
Ferragina A, de los Campos G, Vazquez AI, Cecchinato A, Bittante G. Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data. J Dairy Sci 2015; 98:8133-51. [PMID: 26387015 DOI: 10.3168/jds.2014-9143] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Accepted: 07/06/2015] [Indexed: 11/19/2022]
Abstract
The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.
Collapse
Affiliation(s)
- A Ferragina
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - G de los Campos
- Epidemiology and Biostatistics Department, Michigan State University, East Lansing 48824; Department of Statistics and Probability, Michigan State University, East Lansing 48824
| | - A I Vazquez
- Epidemiology and Biostatistics Department, Michigan State University, East Lansing 48824
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy.
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| |
Collapse
|
47
|
Bergamaschi M, Aprea E, Betta E, Biasioli F, Cipolat-Gotet C, Cecchinato A, Bittante G, Gasperi F. Effects of dairy system, herd within dairy system, and individual cow characteristics on the volatile organic compound profile of ripened model cheeses. J Dairy Sci 2015; 98:2183-96. [DOI: 10.3168/jds.2014-8807] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 12/27/2014] [Indexed: 11/19/2022]
|