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Huang CH, Furukawa K, Kusaba N, Baba T, Kawakami J, Hagiya K. Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins. J Dairy Sci 2024; 107:3738-3752. [PMID: 38246544 DOI: 10.3168/jds.2023-24399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/13/2023] [Indexed: 01/23/2024]
Abstract
In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, termed ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss (ML) estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of the daily heritability estimates over lactation were 0.086 (0.075-0.095) for SCS, 0.104 (0.073-0.127) for ML_SCS_DSCC, 0.137 (0.014-0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115-0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.
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Affiliation(s)
- Che-Hsuan Huang
- Department of Life and Food Science, Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan; Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan
| | - Kenji Furukawa
- Tokachi Federation of Agricultural Cooperatives, Obihiro 080-0022, Japan
| | - Nobuyuki Kusaba
- Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan
| | - Toshimi Baba
- Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo 001-8555, Japan
| | - Junpei Kawakami
- Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo 001-8555, Japan
| | - Koichi Hagiya
- Department of Life and Food Science, Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan.
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Bobbo T, Matera R, Biffani S, Gómez M, Cimmino R, Pedota G, Neglia G. Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes. J Dairy Sci 2024; 107:508-515. [PMID: 37709038 DOI: 10.3168/jds.2023-23629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/17/2023] [Indexed: 09/16/2023]
Abstract
In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.
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Affiliation(s)
- T Bobbo
- Institute of Agricultural Biology and Biotechnology, National Research Council, Via Edoardo Bassini 15, 20133 Milano, Italy
| | - R Matera
- Department of Veterinary Medicine and Animal Production, Federico II University, Via Federico Delpino 1, 80137 Naples, Italy
| | - S Biffani
- Institute of Agricultural Biology and Biotechnology, National Research Council, Via Edoardo Bassini 15, 20133 Milano, Italy.
| | - M Gómez
- Italian National Association of Buffalo Breeders, Via Petrarca, 42-44, 81100 Caserta, Italy
| | - R Cimmino
- Italian National Association of Buffalo Breeders, Via Petrarca, 42-44, 81100 Caserta, Italy
| | - G Pedota
- Associazione Regionale Allevatori della Basilicata, 85100 Potenza, Italy
| | - G Neglia
- Department of Veterinary Medicine and Animal Production, Federico II University, Via Federico Delpino 1, 80137 Naples, Italy
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3
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Tolone M, Mastrangelo S, Scatassa ML, Sardina MT, Riggio S, Moscarelli A, Sutera AM, Portolano B, Negrini R. A First Investigation into the Use of Differential Somatic Cell Count as a Predictor of Udder Health in Sheep. Animals (Basel) 2023; 13:3806. [PMID: 38136843 PMCID: PMC10740685 DOI: 10.3390/ani13243806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Differential somatic cell count (DSCC), the percentage of somatic cell count (SCC) due to polymorphonuclear leukocytes (PMNs) and lymphocytes (LYMs), is a promising effective diagnostic marker for dairy animals with infected mammary glands. Well-explored in dairy cows, DSCC is also potentially valid in sheep, where clinical and subclinical mastitis outbreaks are among the principal causes of culling. We pioneered the application of DSCC in dairy ewes by applying receiver-operating characteristic (ROC) curve analysis to define the most accurate thresholds to facilitate early discrimination of sheep with potential intramammary infection (IMI) from healthy animals. We tested four predefined SCC cut-offs established in previous research. Specifically, we applied SCC cut-offs of 265 × 103 cells/mL, 500 × 103 cells/mL, 645 × 103 cells/mL, and 1000 × 103 cells/mL. The performance of DSCC as a diagnostic test was assessed by examining sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) analyses. The designated threshold value for DSCC in the detection of subclinical mastitis is established at 79.8%. This threshold exhibits Se and Sp of 0.84 and 0.81, accompanied by an AUC of 0.88. This study represents the inaugural exploration of the potential use of DSCC in sheep's milk as an early indicator of udder inflammation.
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Affiliation(s)
- Marco Tolone
- Department of Agricultural Food and Forest Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy; (S.M.); (M.T.S.); (S.R.); (B.P.)
| | - Salvatore Mastrangelo
- Department of Agricultural Food and Forest Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy; (S.M.); (M.T.S.); (S.R.); (B.P.)
| | - Maria Luisa Scatassa
- Istituto Zooprofilattico Sperimentale della Sicilia, Via Gino Marinuzzi 3, 90129 Palermo, Italy;
| | - Maria Teresa Sardina
- Department of Agricultural Food and Forest Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy; (S.M.); (M.T.S.); (S.R.); (B.P.)
| | - Silvia Riggio
- Department of Agricultural Food and Forest Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy; (S.M.); (M.T.S.); (S.R.); (B.P.)
| | - Angelo Moscarelli
- Istituto Sperimentale Zootecnico per la Sicilia, Via Roccazzo 85, 90136 Palermo, Italy;
| | - Anna Maria Sutera
- Department of Chemical, Biological, Pharmaceutical, and Environmental Sciences, University of Messina, 98166 Messina, Italy;
| | - Baldassare Portolano
- Department of Agricultural Food and Forest Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy; (S.M.); (M.T.S.); (S.R.); (B.P.)
| | - Riccardo Negrini
- Associazione Italiana Allevatori, Via Tomassetti Giuseppe 9, 00161 Rome, Italy;
- Department of Animal Science, Food and Nutrition—DIANA, University Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
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Huang CH, Furukawa K, Kusaba N. Estimating the nonlinear interaction between somatic cell score and differential somatic cell count on milk production by parity using generalized additive models. J Dairy Sci 2023; 106:7942-7953. [PMID: 37562643 DOI: 10.3168/jds.2022-22958] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/30/2023] [Indexed: 08/12/2023]
Abstract
This observational study aimed to use somatic cell score (SCS) and differential somatic cell count (DSCC), the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells, to investigate how mastitis affected milk production. Using generalized additive models, we analyzed 50,618 test-day records from 8,081 lactations from 7,912 cows in 197 herds between January 2021 and March 2022 to estimate the nonlinear interaction between SCS and DSCC, and the effects of lactation stages and seasons on milk yield, milk component percentages, and milk component yields by parity of cows. The results show that the interaction between SCS and DSCC on these traits was significant, nonlinear, and complex. When DSCC was high, the negative effects of SCS were minimal, even when SCS reached 8 (i.e., 3,200,000 somatic cells/mL). Cows with high DSCC could have milk yields similar to healthy cows, implying that these cows may have been in the early stages of mastitis and that the milk yield had yet to be affected. Contrastingly, when DSCC was low, milk loss due to high SCS was drastic, especially for cows in third or later lactations, whose milk yield could reduce from more than 35 kg/d to less than 15 kg/d (-59.9%). This tremendous milk loss in high-parity cows was likely due to their higher milk yield and higher risks of chronic mastitis. High SCS and low DSCC also led to a pronounced change in milk composition. The decrease in the percentage of lactose can be directly related to the damage of inflammation to the mammary gland, while the increase in fat and protein percentages was more attributable to the concentration effect resulting from the reduced milk yield. Compared with analyses based on categorized SCS and DSCC values, modeling these 2 indices directly helps us more precisely assess mastitis effects on milk yield and milk composition. For efficient milk production, our results indicate that we should prevent high-parity cows from entering a state of high SCS and low DSCC.
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Affiliation(s)
- Che-Hsuan Huang
- Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, 080-8555 Japan
| | - Kenji Furukawa
- Tokachi Federation of Agricultural Cooperatives, Obihiro, Hokkaido, 080-0013 Japan
| | - Nobuyuki Kusaba
- Field Center of Animal Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, 080-8555 Japan.
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Nudda A, Carta S, Battacone G, Pulina G. Feeding and Nutritional Factors That Affect Somatic Cell Counts in Milk of Sheep and Goats. Vet Sci 2023; 10:454. [PMID: 37505859 PMCID: PMC10385566 DOI: 10.3390/vetsci10070454] [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: 06/02/2023] [Revised: 07/03/2023] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
The purpose of this quantitative review is to highlight the effects of feeding strategies using some mineral, vitamin, marine oil, and vegetable essential oil supplements and some agri-food by-products to reduce SCCs in the milk of sheep and goats. According to the results, only specific dietary factors at specific doses could reduce SCCs in the milk of dairy sheep and goats. The combination of Se and vitamin E in the diet was more effective in sheep than in goats, while the inclusion of polyphenols, which are also present in food matrices such as agro-industrial by-products, led to better results. Some essential oils can be conveniently used to modulate SCCs, although they can precipitate an off-flavoring problem. This work shows that SCCs are complex and cannot be determined using a single experimental factor, as intramammary inflammation, which is the main source of SC in milk, can manifest in a subclinical form without clinical signs. However, attention to mineral and vitamin supplementation, even in the most difficult cases, such as those of grazing animals, and the use of anti-inflammatory substances directly or through by-products, can improve the nutritional condition of animals and reduce their SCCs, offering undeniable benefits for the milk-processing sector as well.
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Affiliation(s)
- Anna Nudda
- Department of Agricultural Sciences, University of Sassari, Viale Italia, 39, 07100 Sassari, Italy
| | - Silvia Carta
- Department of Agricultural Sciences, University of Sassari, Viale Italia, 39, 07100 Sassari, Italy
| | - Gianni Battacone
- Department of Agricultural Sciences, University of Sassari, Viale Italia, 39, 07100 Sassari, Italy
| | - Giuseppe Pulina
- Department of Agricultural Sciences, University of Sassari, Viale Italia, 39, 07100 Sassari, Italy
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6
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Magro S, Costa A, Santinello M, Penasa M, De Marchi M. Udder health-related traits in cow milk: phenotypic variability and effect on milk yield and composition. Animal 2023; 17:100823. [PMID: 37196579 DOI: 10.1016/j.animal.2023.100823] [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: 10/25/2022] [Revised: 04/05/2023] [Accepted: 04/07/2023] [Indexed: 05/19/2023] Open
Abstract
The milk differential somatic cell count (DSCC) has been proposed in recent years as a mean by which to better monitor the udder health status (UHS) in dairy cows. Milk DSCC is the amount of polymorphonuclear neutrophils and lymphocytes contributing to the total somatic cell count (SCC) and can be determined on a routine basis in individual milk samples subjected to official analysis. In the present study, 522 865 milk test-day records of 77 143 cows were scrutinised to identify factors affecting the variability of both DSCC and SCC in Holstein Friesian, Jersey, Simmental and Rendena cows through linear mixed models. The fixed effects were breed, parity, lactation stage, sampling season, and all the first-order interactions of breed. Cow and herd-test-date were considered as random. Subsequently, four UHS groups were created (1: SCC ≤ 200 000 cells/mL and DSCC ≤ 65%; 2: SCC ≤ 200 000 cells/mL and DSCC > 65%; 3: SCC > 200 000 cells/mL and DSCC > 65%; 4: SCC > 200 000 cells/mL and DSCC ≤ 65%) to compare milk yield and quality. Milk SCS and DSCC differed across lactation, parity, sampling season and breed. In particular, Simmental cows had the lowest SCC and Jersey the lowest DSCC. Depending on the breed, UHS affected daily milk yield and composition to a different extent. The UHS group 4, i.e. the one grouping test-day records with high SCC and low DSCC, presented the lowest estimate of milk yield and lactose content no matter the breeds. Our findings support that udder health-related traits (SCS and DSCC) are useful information to improve udder health at individual cow and herd levels. Moreover, the combination of SCS and DSCC is useful to monitor milk yield and composition.
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Affiliation(s)
- S Magro
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35030 Legnaro, Italy
| | - A Costa
- Department of Veterinary Medical Sciences, Alma Mater Studiorum University of Bologna, 40064 Ozzano dell'Emilia, Italy.
| | - M Santinello
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35030 Legnaro, Italy
| | - M Penasa
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35030 Legnaro, Italy
| | - M De Marchi
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35030 Legnaro, Italy
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Magro S, Costa A, De Marchi M. Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition. Animals (Basel) 2023; 13:ani13071249. [PMID: 37048505 PMCID: PMC10093597 DOI: 10.3390/ani13071249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/24/2023] [Accepted: 04/01/2023] [Indexed: 04/14/2023] Open
Abstract
Milk differential somatic cell count (DSCC) represents the percentage of polymorphonuclear neutrophils and lymphocytes out of the total somatic cell count (SCC) and has been proposed in recent years as a proxy for udder health in dairy cows. We investigated phenotypic factors affecting SCC and DSCC using 3978 records of 212 Alpine Grey and 426 Burlina cows farmed in Northern Italy. The linear mixed model accounted for the fixed effects of breed, parity, lactation stage, sampling season, and first-order interactions of breed with the other effects. Cow, herd-test-date nested within breed were random. Subsequently, four udder health status groups (UHS) were created by combining SCC and DSCC to assess the UHS impact on milk yield and quality. DSCC was greater in Alpine Grey (66.2 ± 0.8%) than Burlina cows (63.2 ± 0.6%) and, similarly to SCC, it increased with days in milk and parity regardless of breed. Milk yield and composition were affected by UHS in both breeds. These results suggest that also udder health of local breeds can be monitored on a large scale through SCC and DSCC for reduction in biodiversity loss and increased farm profitability. However, in addition to milk data, the introduction of mastitis recording and monitoring plans is advisable.
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Affiliation(s)
- Silvia Magro
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Padova, Italy
| | - Angela Costa
- Department of Veterinary Medical Sciences, Alma Mater Studiorum University of Bologna, 40064 Bologna, Italy
| | - Massimo De Marchi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020 Padova, Italy
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8
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Bobbo T, Matera R, Pedota G, Manunza A, Cotticelli A, Neglia G, Biffani S. Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes. J Dairy Sci 2023; 106:1942-1952. [PMID: 36586801 DOI: 10.3168/jds.2022-22292] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 09/27/2022] [Indexed: 12/31/2022]
Abstract
Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.
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Affiliation(s)
- T Bobbo
- Consiglio Nazionale delle Ricerche (CNR), Istituto di Biologia e Biotecnologia Agraria (IBBA), 20133 Milan, Italy; Department of Agricultural and Environmental Sciences, University of Milan, 20133 Milan, Italy
| | - R Matera
- Department of Veterinary Medicine and Animal Production, Federico II University, 80137 Naples, Italy
| | - G Pedota
- Associazione Regionale Allevatori della Basilicata, 85100 Potenza, Italy
| | - A Manunza
- Consiglio Nazionale delle Ricerche (CNR), Istituto di Biologia e Biotecnologia Agraria (IBBA), 20133 Milan, Italy
| | - A Cotticelli
- Department of Veterinary Medicine and Animal Production, Federico II University, 80137 Naples, Italy
| | - G Neglia
- Department of Veterinary Medicine and Animal Production, Federico II University, 80137 Naples, Italy.
| | - S Biffani
- Consiglio Nazionale delle Ricerche (CNR), Istituto di Biologia e Biotecnologia Agraria (IBBA), 20133 Milan, Italy
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9
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Huang CH, Kusaba N. Association between differential somatic cell count and California Mastitis Test results in Holstein cattle. JDS COMMUNICATIONS 2022; 3:441-445. [PMID: 36465503 PMCID: PMC9709608 DOI: 10.3168/jdsc.2022-0249] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/02/2022] [Indexed: 06/01/2023]
Abstract
The California Mastitis Test (CMT) has been used to estimate total somatic cell count (SCC) levels in milk; however, milk with similar SCC levels occasionally shows inconsistent CMT results, which limits the use of the CMT. This observational study aimed to investigate how differential cell counts in milk influence the CMT in Holstein cattle through the novel parameters differential somatic cell count (DSCC) and macrophage proportion (MAC). We performed the CMT on d 0, 3, 5, 7, 14, and 21 after identifying mastitis, and simultaneously measured SCC, DSCC, and MAC at the quarter level. We followed 58 mastitis events occurring in 41 cows and obtained 307 quarter-level records after data cleaning. We transformed SCC to somatic cell score (SCS) and MAC to its logarithm to fit the normal distribution and analyzed the data using the cumulative logit mixed model. Results showed that both an increase in SCS (odds ratio: 3.66, 95% confidence interval: 2.89-4.64) and the logarithm of MAC (odds ratio: 4.35, 95% confidence interval: 1.91-9.91) can contribute to a higher CMT score. During the healing process of mastitis, MAC tends to increase as SCC decreases; thus, even samples with low SCC can cause positive CMT reactions. We recommend that practitioners avoid making treatment decisions based on the CMT alone. We also noted that the CMT is sensitive to subclinical mastitis with high MAC, hence it could be considered an alternative to detecting high MAC (chronic) mastitis.
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Widmer J, Descloux L, Brügger C, Jäger ML, Berger T, Egger L. Direct labeling of milk cells without centrifugation for counting total and differential somatic cells using flow cytometry. J Dairy Sci 2022; 105:8705-8717. [PMID: 36175240 DOI: 10.3168/jds.2022-22038] [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: 03/03/2022] [Accepted: 06/27/2022] [Indexed: 11/19/2022]
Abstract
Somatic cell count (SCC) in milk is an essential indicator for defining and managing udder health. However, analyzing differential SCC (dSCC) can be helpful in determining the type or evolution stage of mastitis. A high abundance of polymorphonuclear cells (PMN) is associated with acute mastitis; however, the status of a chronic disease is less well characterized. A method capable of analyzing SCC and dSCC can prove to be a helpful tool for monitoring the status of evolution of mastitis disease in a better way. Therefore, a new direct-flow cytometry method was developed to count and differentiate somatic cells in milk without the steps of centrifugation or washing, avoiding variabilities that occur due to enrichment or loss of specific cell types. In this new method, SCC is analyzed using the method of DNA staining with Hoechst stain, whereas dSCC are analyzed using specific antibodies targeting 2 main cell types associated with mastitis: PMN cells and antigen-presenting cells, which are associated with innate and adaptive immunity. Equivalent SCC values were obtained between the new method and the routine ISO 13366-2 method in a comparison of 240 raw milk samples. Furthermore, dSCC results were confirmed by microscopy after May-Gründwald-Giemsa staining in 165 quarter milk samples from healthy and diseased cows. The method was verified with fluorescence microscopy on the 2 targeted cell types and in raw milk samples. The newly developed method is independent of any instrument and can be further designed to differentiate other cell types and animal species by selecting appropriate antibodies.
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Affiliation(s)
- Jérôme Widmer
- Method Development and Analytics, Agroscope Liebefeld, 3003 Bern, Switzerland
| | - Laurence Descloux
- Method Development and Analytics, Agroscope Liebefeld, 3003 Bern, Switzerland
| | - Cédric Brügger
- Method Development and Analytics, Agroscope Liebefeld, 3003 Bern, Switzerland
| | | | - Thomas Berger
- Method Development and Analytics, Agroscope Liebefeld, 3003 Bern, Switzerland
| | - Lotti Egger
- Method Development and Analytics, Agroscope Liebefeld, 3003 Bern, Switzerland.
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11
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Matera R, Di Vuolo G, Cotticelli A, Salzano A, Neglia G, Cimmino R, D’Angelo D, Biffani S. Relationship among Milk Conductivity, Production Traits, and Somatic Cell Score in the Italian Mediterranean Buffalo. Animals (Basel) 2022; 12:ani12172225. [PMID: 36077945 PMCID: PMC9455038 DOI: 10.3390/ani12172225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
The measurement of milk electrical conductivity (EC) is a relatively simple and inexpensive technique that has been evaluated as a routine method for the diagnosis of mastitis in dairy farms. The aim of this study was to obtain further knowledge on relationships between EC, production traits and somatic cell count (SCC) in Italian Mediterranean Buffalo. The original dataset included 5411 records collected from 808 buffalo cows. Two mixed models were used to evaluate both the effect of EC on MY, PP and FP and EC at test-day, and the effect of EC on somatic cell score (SCS) by using five different parameters (EC_param), namely: EC collected at the official milk recording test day (EC_day0), EC collected 3 days before official milk recording (EC_day3), and three statistics calculated from EC collected 1, 3 and 5 days before each test-day, respectively. All effects included in the model were significant for all traits, with the only exception of the effect of EC nested within parity for FP. The relationship between EC and SCS was always positive, but of different magnitude according to the parity. The regression of EC on SCS at test-day using different EC parameters was always significant except when the regression parameter was the slope obtained from a linear regression of EC collected over the 5-day period. Moreover, in order to evaluate how well the different models fit the data, three parameters were used: the Average Information Criteria (AIC), the marginal R2 and the conditional R2. According to AIC and to both the Marginal and Conditional R2, the best results were obtained when the regression parameter was the mean EC estimated over the 5-day period.
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Affiliation(s)
- Roberta Matera
- Dipartimento di Medicina Veterinaria e Produzioni Animali, Università degli Studi di Napoli Federico II, 80131 Naples, Italy
| | - Gabriele Di Vuolo
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy
| | - Alessio Cotticelli
- Dipartimento di Medicina Veterinaria e Produzioni Animali, Università degli Studi di Napoli Federico II, 80131 Naples, Italy
| | - Angela Salzano
- Dipartimento di Medicina Veterinaria e Produzioni Animali, Università degli Studi di Napoli Federico II, 80131 Naples, Italy
- Correspondence:
| | - Gianluca Neglia
- Dipartimento di Medicina Veterinaria e Produzioni Animali, Università degli Studi di Napoli Federico II, 80131 Naples, Italy
| | - Roberta Cimmino
- Associazione Nazionale Allevatori Specie Bufalina (ANASB), 81100 Caserta, Italy
| | - Danila D’Angelo
- Dipartimento di Medicina Veterinaria e Produzioni Animali, Università degli Studi di Napoli Federico II, 80131 Naples, Italy
| | - Stefano Biffani
- Istituto di Biologia e Biotecnologia Agraria (IBBA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
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12
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Mariani E, Cipolat‐Gotet C, Stefanon B, Zecconi A, Stocco G, Sandri M, Ablondi M, Mountricha M, Summer A. Effect of total and differential somatic cell count on yield, composition and predicted coagulation properties from individual dairy cows. INT J DAIRY TECHNOL 2022. [DOI: 10.1111/1471-0307.12857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Elena Mariani
- Department of Veterinary Science University of Parma Parma 43126Italy
| | | | - Bruno Stefanon
- Department of AgroFood, Environmental and Animal Science University of Udine Udine 33100Italy
| | - Alfonso Zecconi
- Department of Biomedical Surgical and Dental Sciences One Health Unit University of Milano Milano 20133 Italy
| | - Giorgia Stocco
- Department of Veterinary Science University of Parma Parma 43126Italy
| | - Misa Sandri
- Department of AgroFood, Environmental and Animal Science University of Udine Udine 33100Italy
| | - Michela Ablondi
- Department of Veterinary Science University of Parma Parma 43126Italy
| | - Maria Mountricha
- Department of Veterinary Science University of Parma Parma 43126Italy
| | - Andrea Summer
- Department of Veterinary Science University of Parma Parma 43126Italy
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13
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Bobbo T, Meoni G, Niero G, Tenori L, Luchinat C, Cassandro M, Penasa M. Nuclear magnetic resonance spectroscopy to investigate the association between milk metabolites and udder quarter health status in dairy cows. J Dairy Sci 2021; 105:535-548. [PMID: 34656344 DOI: 10.3168/jds.2021-20906] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 08/25/2021] [Indexed: 01/16/2023]
Abstract
Nuclear magnetic resonance spectroscopy was applied to investigate the association between milk metabolome and udder quarter health status in dairy cows. Mammary gland health status was defined by combining information provided by traditional somatic cell count (SCC) and differential SCC (DSCC), which expresses the percentage of neutrophils and lymphocytes over total SCC. Quarter milk samples were collected in triplicate (d 1 to 3) from 10 Simmental cows, 5 defined as cases and 5 defined as controls according to SCC levels at d 0. A total of 120 samples were collected and analyzed for bacteriology, milk composition, SCC, DSCC, and milk metabolome. Bacteriological analysis revealed the presence of mostly coagulase-negative staphylococci in quarter milk samples of cows defined as cases. Nuclear magnetic resonance spectra of all quarter samples were first analyzed using the unsupervised multivariate approach principal component analysis, which revealed a specific metabolomic fingerprint of each cow. Then, the supervised cross-validated orthogonal projections to latent structures discriminant analysis unquestionably showed that each cow could be very well identified according to its milk metabolomic fingerprint (accuracy = 95.8%). The comparison of 12 different models, built on bucketed 1-dimensional NOESY spectra (noesygppr1d, Bruker BioSpin) using different SCC and DSCC thresholds, corroborated the assumption of improved udder health status classification ability by joining information provided by both SCC and DSCC. Univariate analysis performed on the 34 quantitated metabolites revealed lower levels of riboflavin, galactose, galactose-1-phosphate, dimethylsulfone, carnitine, hippurate, orotate, lecithin, succinate, glucose, and lactose, and greater levels of lactate, phenylalanine, choline, acetate, O-acetylcarnitine, 2-oxoglutarate, and valine, in milk samples with high somatic cells. In the 5 cases, results of the udder quarter with the highest SCC compared with its symmetrical relative were in line with quarter-level findings. Our study suggests that increased SCC is associated with changes in milk metabolite fingerprint and highlights the potential use of different metabolites as novel indicators of udder health status and milk quality.
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Affiliation(s)
- T Bobbo
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
| | - G Meoni
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff," University of Florence, 50019 Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - G Niero
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy.
| | - L Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff," University of Florence, 50019 Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - C Luchinat
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff," University of Florence, 50019 Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - M Cassandro
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy; Associazione Nazionale Allevatori della Razza Frisona, Bruna e Jersey Italiana, 26100 Cremona (CR), Italy
| | - M Penasa
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, 35020 Legnaro (PD), Italy
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14
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Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows. Sci Rep 2021; 11:13642. [PMID: 34211046 PMCID: PMC8249463 DOI: 10.1038/s41598-021-93056-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/21/2021] [Indexed: 11/29/2022] Open
Abstract
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.
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15
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Scarsella E, Zecconi A, Cintio M, Stefanon B. Characterization of Microbiome on Feces, Blood and Milk in Dairy Cows with Different Milk Leucocyte Pattern. Animals (Basel) 2021; 11:ani11051463. [PMID: 34069719 PMCID: PMC8160755 DOI: 10.3390/ani11051463] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Mastitis is an inflammation of the mammary gland caused by microorganisms and associated with an altered immune response. Recently, several studies hypothesized that a translocation of some bacteria from the gastrointestinal tract to the mammary gland can occur and that this bacterial crossing could be the cause of certain mastitis. The aim of this research is to investigate the bacteria translocation from the gut to the mammary gland, the so-called entero-mammary pathway, through the study of the fecal, blood and milk microbiome. Cows were recruited on the basis of their mammary gland health status and classified as healthy, at risk of mastitis and with mastitis. The microbial composition of feces, blood and milk were analyzed through high-throughput sequencing technique and the results were checked through a quantitative real-time PCR analysis. Although small differences were found in the microbiome of these three specimens between the groups of animals, beta biodiversity, that is, the ratio between whole and individual species diversity, highlighted a microbial community change in the milk of cows with different udder health conditions. The three matrices shared a high number of taxa; however, our results do not confirm a bacterial crossing from gut to milk, that still remains hypothetical. Abstract Mastitis is an inflammatory disease of the mammary gland, caused by the invasion of microorganism on this site, associated with an altered immune response. Recent studies in this field hypothesize that the origin of these pathogens can also be from the gastrointestinal tract, through the entero-mammary pathway in relation to an increase in gut permeability. In this study, we wanted to investigate if inflammatory status of the mammary gland is related to an alteration of gut permeability. The microbiome of feces, blood and milk of lactating cows, recruited on the basis of the total somatic cell count and of the percentage of polymorphonuclear neutrophils and lymphocytes, was studied. Cows were divided into healthy (G), at risk of mastitis (Y) and with mastitis (R) classifications. The bacterial DNA was extracted and the V3 and V4 regions of 16S rRNA sequenced. Moreover, the quantification of total bacteria was performed with quantitative real-time PCR. A non-parametric Kruskal–Wallis test was applied at the phylum, family and genera levels and beta biodiversity was evaluated with the unweighted UniFrac distance metric. Significant differences between groups were found for the microbial composition of feces (Clostridiaceae, Turicibacteriaceae for family level and Clostridium, Dorea, SMB53 and Turicibacter for genus level), blood (Tenericutes for phylum level and Mycoplasma for genus level) and milk (OD1 and Proteobacteria for phylum level, Enterobacteriaceae and Moraxallaceae for family level and Olsenella and Rhodococcus for genus level). The beta biodiversity of feces and blood did not change between groups. Significant differences (p < 0.05) were observed between the beta diversity in milk of G group and Y group and between Y group and R group. The number of taxa in common between feces, blood and milk were 8 at a phylum, 19 at a family and 15 at a genus level. From these results, the bacterial crossing from gut to milk in cows was not confirmed but remained hypothetical and deserves further investigation.
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Affiliation(s)
- Elisa Scarsella
- Department of Agriculture, Food, Environmental and Animal Science, University of Udine, 33100 Udine, Italy; (E.S.); (M.C.)
| | - Alfonso Zecconi
- Department of Biomedical, Surgical and Dental Sciences–One Health Unit, University of Milan, 20100 Milan, Italy;
| | - Michela Cintio
- Department of Agriculture, Food, Environmental and Animal Science, University of Udine, 33100 Udine, Italy; (E.S.); (M.C.)
| | - Bruno Stefanon
- Department of Agriculture, Food, Environmental and Animal Science, University of Udine, 33100 Udine, Italy; (E.S.); (M.C.)
- Correspondence:
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16
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Niero G, Bobbo T, Callegaro S, Visentin G, Pornaro C, Penasa M, Cozzi G, De Marchi M, Cassandro M. Dairy Cows' Health during Alpine Summer Grazing as Assessed by Milk Traits, Including Differential Somatic Cell Count: A Case Study from Italy. Animals (Basel) 2021; 11:ani11040981. [PMID: 33915759 PMCID: PMC8067137 DOI: 10.3390/ani11040981] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/07/2021] [Accepted: 03/25/2021] [Indexed: 11/23/2022] Open
Abstract
Simple Summary Dairy herds in alpine areas usually adopt summer grazing, mainly to reduce feeding costs. This practice is related to the maintenance of local traditions and to the manufacturing of niche dairy products. However, it is important to assess the impact of this practice on cattle health. This case study investigated how milk-related health traits vary across extensive grazing during the summer period, using data collected in a dairy herd whose cows were repeatedly controlled for individual milk samples. Although the transition from barn farming to pasture led to a reduction in milk production, proper grazing management can make dairy cows more resilient in terms of udder health and metabolic efficiency. Findings of the present research report suggested that pasture can be adopted to maintain dairy herd sustainability without impairing animal health. Abstract Extensive summer grazing is a dairy herd management practice frequently adopted in mountainous areas. Nowadays, this activity is threatened by its high labour demand, but it is fundamental for environmental, touristic and economic implications, as well as for the preservation of social and cultural traditions. Scarce information on the effects of such low-input farming systems on cattle health is available. Therefore, the present case study aimed at investigating how grazing may affect the health status of dairy cows by using milk traits routinely available from the national milk recording scheme. The research involved a dairy herd of 52 Simmental and 19 Holstein × Simmental crossbred cows. The herd had access to the pasture according to a rotational grazing scheme from late spring up to the end of summer. A total of 616 test day records collected immediately before and during the grazing season were used. Individual milk yield was registered during the milking procedure. Milk samples were analysed for composition (fat, protein, casein and lactose contents) and health-related milk indicators (electrical conductivity, urea and β-hydroxybutyrate) using mid-infrared spectroscopy. Somatic cell count (SCC) and differential SCC were also determined. Data were analysed with a linear mixed model, which included the fixed effects of the period of sampling, cow breed, stage of lactation and parity, and the random effects of cow nested within breed and the residual. The transition from barn farming to pasture had a negative effect on milk yield, together with a small deterioration of fat and protein percentages. Health-related milk indicators showed a minor deterioration of the fat to protein ratio, differential SCC and electrical conductivity, particularly towards the end of the grazing season, whereas the somatic cell score and β-hydroxybutyrate were relatively constant. Overall, the study showed that, when properly managed, pasture grazing does not have detrimental effects on dairy cows in terms of udder health and efficiency. Therefore, the proper management of cows on pasture can be a valuable solution to preserve the economic, social and environmental sustainability of small dairy farms in the alpine regions, without impairing cows’ health.
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Affiliation(s)
- Giovanni Niero
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
| | - Tania Bobbo
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
| | - Simone Callegaro
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
| | - Giulio Visentin
- Department of Veterinary Medical Sciences, Alma Mater Studiorum University of Bologna, Via Tolara di Sopra 50, 40064 Ozzano dell’Emilia, Italy
- Correspondence: ; Tel.: +39-051-20-97047
| | - Cristina Pornaro
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
| | - Mauro Penasa
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
| | - Giulio Cozzi
- Department of Animal Medicine, Production and Health, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy;
| | - Massimo De Marchi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
| | - Martino Cassandro
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (G.N.); (T.B.); (S.C.); (C.P.); (M.P.); (M.D.M.); (M.C.)
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17
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Bobbo T, Penasa M, Cassandro M. Genetic Parameters of Bovine Milk Fatty Acid Profile, Yield, Composition, Total and Differential Somatic Cell Count. Animals (Basel) 2020; 10:E2406. [PMID: 33339148 PMCID: PMC7765606 DOI: 10.3390/ani10122406] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 12/09/2020] [Accepted: 12/13/2020] [Indexed: 11/16/2022] Open
Abstract
The growing interest of consumers for milk and dairy products of high nutritional value has pushed researchers to evaluate the feasibility of including fatty acids (FA) in selection programs to modify milk fat profile and improve its nutritional quality. Therefore, the aim of this study was to estimate genetic parameters of FA profile predicted by mid-infrared spectroscopy, milk yield, composition, and total and differential somatic cell count. Edited data included 35,331 test-day records of 25,407 Italian Holstein cows from 652 herds. Variance components and heritability were estimated using single-trait repeatability animal models, whereas bivariate repeatability animal models were used to estimate genetic and phenotypic correlations between traits, including the fixed effects of stage of lactation, parity, and herd-test-date, and the random effects of additive genetic animal, cow permanent environment and the residual. Heritabilities and genetic correlations obtained in the present study reflected both the origins of FA (extracted from the blood or synthesized de novo by the mammary gland) and their grouping according to saturation or chain length. In addition, correlations among FA groups were in line with correlation among individual FA. Moderate negative genetic correlations between FA and milk yield and moderate to strong positive correlations with fat, protein, and casein percentages suggest that actual selection programs are currently affecting all FA groups, not only the desired ones (e.g., polyunsaturated FA). The absence of association with differential somatic cell count and the weak association with somatic cell score indicate that selection on FA profile would not affect selection on resistance to mastitis and vice versa. In conclusion, our findings suggest that genetic selection on FA content is feasible, as FA are variable and moderately heritable. Nevertheless, in the light of correlations with other milk traits estimated in this study, a clear breeding goal should first be established.
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Affiliation(s)
- Tania Bobbo
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy; (M.P.); (M.C.)
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