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Lou W, Bonfatti V, Bovenhuis H, Shi R, van der Linden A, Mulder HA, Liu L, Wang Y, Ducro B. Prediction of likelihood of conception in dairy cows using milk mid-infrared spectra collected before the first insemination and machine learning algorithms. J Dairy Sci 2024; 107:9415-9425. [PMID: 38825141 DOI: 10.3168/jds.2023-24621] [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: 12/28/2023] [Accepted: 04/15/2024] [Indexed: 06/04/2024]
Abstract
Accurate and ex-ante prediction of cows' likelihood of conception (LC) based on milk composition information could improve reproduction management on dairy farms. Milk composition is already routinely measured by mid-infrared (MIR) spectra, which are known to change with advancing stages of pregnancy. For lactating cows, MIR spectra may also be used for predicting the LC. Our objectives were to classify the LC at first insemination using milk MIR spectra data collected from calving to first insemination and to identify the spectral regions that contribute the most to the prediction of LC at first insemination. After quality control, 4,866 MIR spectra, milk production, and reproduction records from 3,451 Holstein cows were used. The classification accuracy and area under the curve (AUC) of 6 models comprising different predictors and 3 machine learning methods were estimated and compared. The results showed that partial least square discriminant analysis (PLS-DA) and random forest had higher prediction accuracies than logistic regression. The classification accuracy of good and poor LC cows and AUC in herd-by-herd validation of the best model were 76.35% ± 10.60% and 0.77 ± 0.11, respectively. All wavenumbers with values of variable importance in the projection higher than 1.00 in PLS-DA belonged to 3 spectral regions, namely from 1,003 to 1,189, 1,794 to 2,260, and 2,300 to 2,660 cm-1. In conclusion, the model can predict LC in dairy cows from a high productive TMR system before insemination with a relatively good accuracy, allowing farmers to intervene in advance or adjust the insemination schedule for cows with a poor predicted LC.
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Affiliation(s)
- W Lou
- State Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory of Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China; Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands; Wageningen University & Research, Animal Production Systems, 6700 AH Wageningen, the Netherlands
| | - V Bonfatti
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, 35020, Italy.
| | - H Bovenhuis
- Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - R Shi
- State Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory of Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China; Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands; Wageningen University & Research, Animal Production Systems, 6700 AH Wageningen, the Netherlands
| | - A van der Linden
- Wageningen University & Research, Animal Production Systems, 6700 AH Wageningen, the Netherlands
| | - H A Mulder
- Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - L Liu
- Beijing Dairy Cattle Center, Beijing, 100192, China
| | - Y Wang
- State Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory of Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| | - B Ducro
- Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
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Lou W, Lu H, Ren X, Zhao X, Wang Y, Bonfatti V. Standardization method, testing scenario, and accuracy of the infrared prediction model affect the standardization accuracy of milk mid-infrared spectra. J Dairy Sci 2024; 107:9404-9414. [PMID: 38825120 DOI: 10.3168/jds.2023-24472] [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: 11/26/2023] [Accepted: 04/12/2024] [Indexed: 06/04/2024]
Abstract
The widespread use of milk mid-infrared (MIR) spectroscopy for phenotype prediction has urged the application of prediction models across regions and countries. Spectra standardization is the most effective way to reduce the variability in the spectral signal provided by different instruments and labs. This study aimed to develop different standardization models for MIR spectra collected by multiple instruments, across 2 provinces of China, and investigate whether the standardization method (piecewise direct standardization, PDS, and direct standardization, DS), testing scenario (standardization of spectra collected on the same day or after 7 mo), infrared prediction model accuracy (high or low), and instrument (6 instruments from 2 brands) affect the performance of the standardization model. The results showed that the determination coefficient (R2) between absorbance values at each wavenumber provided by the primary and the secondary instruments increased from less than 0.90 to nearly 1.00 after standardization. Both PDS and DS successfully reduced spectra variation among instruments, and performed significantly better than nonstandardization. However, DS was more prone to overfitting than PDS. Standardization accuracy was higher when tested using spectra collected on the same day compared with those collected 7 mo after, but great improvement in model transferability was obtained for both scenarios compared with the nonstandardized spectra. The less accurate infrared prediction model (for C8:0 and C10:0 content) benefited the most from spectra standardization compared with the more accurate model (for total fat and protein content). For spectra collected 7 mo after standardization, after PDS the root mean square error between predictions obtained by different machines decreased on average by 86% and 94% compared with the values before standardization for C8:0 and C10:0, respectively. The secondary instrument had no significant effect on the R2 between predictions. The variation in the spectral signal provided by different instruments was successfully reduced by standardization across 2 provinces in China. This study lays the foundations for developing a national MIR spectra database to provide consistent predictions across provinces to be used in dairy farm management and breeding programs in China. Additionally, this provides opportunities for data exchange and cooperation at international levels.
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Affiliation(s)
- W Lou
- State Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory of Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - H Lu
- Beijing Consortium for Innovative Bio-Breeding, Beijing 101206, China
| | - X Ren
- Henan Dairy Herd Improvement Center, Zhengzhou 450046, China
| | - X Zhao
- Shandong Ox Livestock Breeding Co. Ltd., Jinan 250100, China
| | - Y Wang
- State Key Laboratory of Animal Biotech Breeding, National Engineering Laboratory of Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
| | - V Bonfatti
- Department of Comparative Biomedicine and Food Science, University of Padova, 35020 Legnaro (PD), Italy
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3
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Valldecabres A, Horan L, Masson J, García-Muñoz A, Pinedo P, Dineen M, Hendriks SJ. Milk component ratios and their associations with energy balance indicators and serum calcium concentration in early-lactation spring-calving pasture-based dairy cows. J Dairy Sci 2024:S0022-0302(24)01109-3. [PMID: 39245160 DOI: 10.3168/jds.2024-24760] [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: 02/06/2024] [Accepted: 08/06/2024] [Indexed: 09/10/2024]
Abstract
Indirect assessment of metabolic status using milk samples provides a non-invasive and objective tool for cow-level health monitoring. Milk fat-to-protein ratio (FPR) has been commonly evaluated as an indirect measure for negative energy balance (EB) in confined dairy cows. However, milk component ratios have not been explored for their association with pasture-based cows' metabolic status. The objectives of this observational study were to 1) describe milk component ratios from 0 to 45 d postpartum, 2) evaluate the associations between milk component ratios [FPR, fat-to-lactose (FLR), protein-to-lactose (PLR)] and indicators of EB (serum β-hydroxybutyrate (BHB) concentration at 5-45 d postpartum and body condition score (BCS) change during the transition period), and 3) evaluate the associations between milk component ratios and serum Ca concentration 0-4 d postpartum in spring-calving dairy cows from pasture-based commercial farms. Milk component ratios were determined on samples collected before AM or PM milkings from 548 cows at 0-45 d postpartum (n = 970). Serum BHB and Ca determinations were performed in blood samples collected at the time of milk sample collection at 5-45 d postpartum (n = 918) and 0-4 d postpartum (n = 50), respectively; and BCS change was calculated using BCS assigned between 29 d prepartum and 45 d postpartum (n = 851). Cows' calving date, parity (1st, 2nd-3rd or ≥ 4th) and breed (Holstein-Friesian or dairy crossbred) information was obtained from the farm records. Data was analyzed by multiple linear regression. Average milk FPR, FLR and PLR were 0.70, 0.53 and 0.72, respectively. Milk FPR linearly increased while milk FLR linearly decreased postpartum both at a rate of 0.004 units per day; milk PLR decreased 0.05 units per day for the first 30 d postpartum and moderately increased afterward. Milk FPR and FLR were 0.71 and 0.52 units lower before AM than PM milking, respectively; while milk PLR was similar before AM and PM milking. Milk FPR and FLR were 0.07 to 0.10 units higher for 2nd-3rd compared with 1st and ≥ 4th parity cows. Milk PLR was 0.03 units greater for ≥ 4th compared with 2nd-3rd and 1st parity cows. Further, crossbred cows had 0.07, 0.08 and 0.03 higher milk FPR, FLR and PLR than Holstein-Friesian cows, respectively. Moderate to high P-values along with moderate to small estimated slopes and wide 95% confidence intervals were observed for the associations between milk component ratios and indicators of EB. A positive linear association was observed between milk FPR and serum Ca concentration within 4 d postpartum; milk FPR increased 0.31 units per each mmol/L increase in serum Ca concentration. Cows with low serum Ca concentration within 4 d postpartum had 0.27 units lower milk FPR compared with cows at or above the threshold (2.12 mmol/L), and tended to have 0.15 units lower milk FPR compared with cows at or above the threshold (2.00 mmol/L). In conclusion, further research is needed to reach conclusions on the association between milk component ratios determined before milking and EB indicators. The potential of milk FPR for monitoring blood Ca status warrants further investigation in early-lactation pasture-based dairy cows.
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Affiliation(s)
- A Valldecabres
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61 C996.
| | - L Horan
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61 C996
| | - J Masson
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61 C996
| | - A García-Muñoz
- Facultad de Veterinaria, Universidad Cardenal Herrera-CEU, CEU Universities, Valencia, Spain
| | - P Pinedo
- Department of Animal Sciences, Colorado State University, Fort Collins, CO 80523
| | - M Dineen
- Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland P61 C996
| | - S J Hendriks
- DairyNZ Ltd., 24 Millpond Lane, Lincoln 7608, New Zealand
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Magro S, Sneddon NW, Costa A, Chiarin E, Penasa M, De Marchi M. Does the age of milk affect its mid-infrared spectrum and predictions? Food Chem 2024; 441:138355. [PMID: 38219360 DOI: 10.1016/j.foodchem.2024.138355] [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: 09/18/2023] [Revised: 12/15/2023] [Accepted: 01/01/2024] [Indexed: 01/16/2024]
Abstract
Milk of dairy species commonly undergo standardized official analyses, these that may require chemical preservation and transportation to a certified laboratory. In this context, storage duration is an important factor that can potential affect both milk chemical analyses and its mid-infrared spectrum. We analysed milk samples at different time points/ages to assess repeatability and reproducibility of mid-infrared predicted traits (e.g., fat and protein). Using spectral data, we also evaluated the ability of spectroscopy coupled with chemometrics to discriminate samples according to their age. Although the main components of milk remained consistently reproducible across age (days), changes in the spectrum due to sample aging and deterioration of the matrix were detectable. Using a discriminant analysis, we achieved a classification accuracy of 77% in validation. Predicting milk age using mid-infrared spectra is feasible, allowing for sample monitoring within circuits where maximum reliability is needed, e.g., bulk or individual milk samples for legal/official use or payment systems.
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Affiliation(s)
- S Magro
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - N W Sneddon
- School of Agriculture and Environment, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand
| | - A Costa
- Department of Veterinary Medical Sciences, University of Bologna, Via Tolara di Sopra 43, 40064 Ozzano dell'Emilia (BO), Italy.
| | - E Chiarin
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - M Penasa
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - M De Marchi
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
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Il Y, Il D, Zabolotnykh M, Savenkova I, Nurzhanova K, Zhantleuov D, Kozhebayev B, Akhmetova B, Satiyeva K, Kurmangali L. Changes in blood biochemical parameters in highly productive cows with ketosis. Vet World 2024; 17:1130-1138. [PMID: 38911074 PMCID: PMC11188885 DOI: 10.14202/vetworld.2024.1130-1138] [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: 10/21/2023] [Accepted: 04/26/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Aim Biochemical blood testing is the main diagnostic indicator of the clinical condition of highly productive animals and a method of determining changes in metabolic disorders. This study focuses on metabolic changes (ketosis), which are of the utmost importance in the assessment of the health status of animals, as well as differences in intergroup characteristics. The main focus of this study is to demonstrate the influence of subclinical ketosis in highly productive cows on changes in biochemical blood parameters during different physiological periods to further prevent this disease, adjust feeding rations, and prevent premature culling of animals. This study aimed to evaluate and establish changes in the biochemical status dynamics of highly productive cows with metabolic disorders in an industrial livestock complex. Materials and Methods Blood samples were systematically collected from highly productive cows of the Simmental breed (n = 60) and served as the primary material for subsequent analyses. Each methodological step was designed to ensure evaluation of the metabolic changes associated with post-calving adjustments in highly productive dairy cows. This study employed a comprehensive approach integrating clinical assessments, laboratory analyses, biochemical evaluations, instrumental measurements, and statistical analyses. Results A biochemical blood test showed that the number of ketone bodies in the experimental group exceeded the norm, varied depending on the physiological state of the animals, and ranged from 0.89 to 1.45 mmol/L. At 10 days after calving, the highest indicator was 1.45 ± 0.05 mmol/L. This indicator was 1.05 mmol/L higher than that in the control group and exceeded the norm by 0.95. Conclusion Excess ketone bodies in the blood of animals led to accumulation in urine and milk, indicating a disturbance in metabolic processes in the body and a decrease in the quality of animal husbandry products. The sample size and the focus on a single breed from one geographical location may limit the generalizability of the findings. Further research should explore the mechanistic bases of ketosis development, potentially integrating genomic and proteomic approaches to understand the genetic predispositions and molecular pathways involved.
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Affiliation(s)
- Yelena Il
- Department of Food Security, Agrotechnological Faculty, Manash Kozybaev North Kazakhstan University, Petropavlovsk, Kazakhstan
| | - Dmitrii Il
- Department of Food Security, Agrotechnological Faculty, Manash Kozybaev North Kazakhstan University, Petropavlovsk, Kazakhstan
| | - Mikhail Zabolotnykh
- Department of Veterinary and Sanitary Expertise of Livestock Product and Hygiene of Farm Animals, Faculty of Veterinary Medicine, P.A. Stolypin Omsk State Agrarian University, Omsk, Russian Federation
| | - Inna Savenkova
- Department of Agronomy and Forestry, Agrotechnological Faculty, Manash Kozybaev North Kazakhstan University, Petropavlovsk, Kazakhstan
| | - Kulsara Nurzhanova
- Department of Agriculture and Bioresources, Faculty of Veterinary Medicine and Agricultural Management, Shakarim University of Semey, Semey, Kazakhstan
| | - Daniyar Zhantleuov
- Department of Livestock, North Kazakhstan Research Institute of Agriculture, Beskol, Kazakhstan
| | - Bolatpek Kozhebayev
- Department of Agriculture and Bioresources, Faculty of Veterinary Medicine and Agricultural Management, Shakarim University of Semey, Semey, Kazakhstan
| | - Balnur Akhmetova
- Department of Agriculture and Bioresources, Faculty of Veterinary Medicine and Agricultural Management, Shakarim University of Semey, Semey, Kazakhstan
| | - Kaliya Satiyeva
- Department of Agriculture and Bioresources, Faculty of Veterinary Medicine and Agricultural Management, Shakarim University of Semey, Semey, Kazakhstan
| | - Lailim Kurmangali
- Department of Agriculture and Bioresources, Faculty of Veterinary Medicine and Agricultural Management, Shakarim University of Semey, Semey, Kazakhstan
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De Jong E, Rijpert-Duvivier A, Veldman H, Steeneveld W, Jorritsma R. Milk β-hydroxybutyrate metrics and its consequences for surveillance of hyperketonaemia on commercial dairy farms. Front Vet Sci 2023; 10:1272162. [PMID: 38026643 PMCID: PMC10663411 DOI: 10.3389/fvets.2023.1272162] [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: 08/04/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Dairy cows that are unable to adapt to a change in their metabolic status are at risk for hyperketonaemia (HK). Reported HK herd level prevalences range a lot and we hypothesized that this is partly due to differences in used tests and monitoring protocols. Insights in milk β-hydroxybutyrate (BHB) metrics can potentially explain why the reported incidences or prevalences vary between test strategies. Automated collection and repeated analyses of individual milk samples with the DeLaval Herd Navigator™ (HN) provides real-time data on milk BHB concentrations. We aimed to use that information to gain insight in BHB metrics measured in milk from 3 to 60 days in milk (DIM). Using different cut-offs (0.08, 0.10 and 0.15 mmol/L), 5 BHB metrics were determined. Furthermore, the impact of 4 arbitrary test protocols on the detected incidence of HK was assessed. We used HN data of 3,133 cows from 35 herds. The cumulative incidence of HK between 3 and 60 DIM varied between 30.5 and 76.7% for different cut-off values. We found a higher HK incidence for higher parity cows. The first elevated BHB concentrations were roughly found between one and two weeks after calving. For higher parity cows the maximum BHB concentrations were higher, the onset of HK was earlier after calving, and the number of episodes of HK was higher. It appeared that the sensitivity of a HK test protocol can be increased by increasing the testing frequency from once to twice a week. Also extending the number of days of the test window from 4-14 to 4-21 days enhances the chance to find cows experiencing HK. In conclusion, HN data provided useful insights in milk BHB metrics. The chosen cut-off value had a large effect on the reported metrics which explains why earlier reported incidences or prevalences vary such a lot. Differences in test period and sample selection also had a large impact on the observed HK incidence. We suggest to take this in consideration while evaluating whether HK is an issue on farm level and use a uniform protocol for benchmarking of HK between farms.
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Affiliation(s)
- Elise De Jong
- Northern Country Animal Care, Cobram, VIC, Australia
| | | | | | - Wilma Steeneveld
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Ruurd Jorritsma
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
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Kostensalo J, Lidauer M, Aernouts B, Mäntysaari P, Kokkonen T, Lidauer P, Mehtiö T. Short communication: Predicting blood plasma non-esterified fatty acid and beta-hydroxybutyrate concentrations from cow milk-addressing systematic issues in modelling. Animal 2023; 17:100912. [PMID: 37566930 DOI: 10.1016/j.animal.2023.100912] [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: 02/20/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 08/13/2023] Open
Abstract
Negative energy status in early lactation is linked to a variety of metabolic disorders, reduced fertility, and decreased milk production. To improve the energy status of cows by breeding and management, the identification of negative energy status is crucial. While biomarkers such as non-esterified fatty acid (NEFA) concentration and beta-hydroxybutyrate (BHB) in blood plasma could be used to identify a negative energy state, measuring them directly from blood is both invasive and expensive. In this work, we developed prediction equations for blood plasma NEFA and BHB levels based on mid-IR spectral measurements of milk. The models were fitted using partial least squares regression and evaluated using both cross-validation and independent-herd validation. A total of 3 183 spectral records from 606 lactations originating from three different herds were utilised. R2 values of 0.53 (RMSE = 0.206 mmol/l, RMSE of cross-validation (RMSECV) 0.217 mmol/l) for NEFA and 0.63 (RMSE = 0.326 mmol/l, RMSECV = 0.353 mmol/l) for BHB were obtained. Furthermore, relatively similar prediction accuracies were found for BHB (RMSE of prediction (RMSEP) 0.411 mmol/l and 0.422 mmol/l) and NEFA (RMSEP = 0.186 mmol/l and 0.221 mmol/l) when model training was done using two herds and validated on the third herd. The results from the model fits confirm that it is possible to build blood plasma BHB and NEFA models based on mid-IR spectra that are sufficiently accurate for practical use.
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Affiliation(s)
- Joel Kostensalo
- Natural Resources Institute Finland, Yliopistokatu 6B, FI-80100 Joensuu, Finland.
| | - Martin Lidauer
- Natural Resources Institute Finland, Tietotie 4, FI-31600 Jokioinen, Finland
| | - Ben Aernouts
- KU Leuven, Biosystems Department, Division of Animal and Human Health Engineering, Livestock Technology Research Group, 2440 Geel, Belgium
| | - Päivi Mäntysaari
- Natural Resources Institute Finland, Tietotie 4, FI-31600 Jokioinen, Finland
| | - Tuomo Kokkonen
- University of Helsinki, Department of Agricultural Sciences, P.O. Box 28, FI-00014 University of Helsinki, Finland
| | - Paula Lidauer
- Natural Resources Institute Finland, Tietotie 4, FI-31600 Jokioinen, Finland
| | - Terhi Mehtiö
- Natural Resources Institute Finland, Tietotie 4, FI-31600 Jokioinen, Finland
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8
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Chowdhury CR, Kavitake D, Jaiswal KK, Jaiswal KS, Reddy GB, Agarwal V, Shetty PH. NMR-based metabolomics as a significant tool for human nutritional research and health applications. FOOD BIOSCI 2023. [DOI: 10.1016/j.fbio.2023.102538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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9
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Giannuzzi D, Mota LFM, Pegolo S, Tagliapietra F, Schiavon S, Gallo L, Marsan PA, Trevisi E, Cecchinato A. Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle. J Dairy Sci 2023; 106:3321-3344. [PMID: 37028959 DOI: 10.3168/jds.2022-22454] [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: 06/28/2022] [Accepted: 12/14/2022] [Indexed: 04/09/2023]
Abstract
The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.
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Affiliation(s)
- Diana Giannuzzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy.
| | - Lucio Flavio Macedo Mota
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy
| | - Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy
| | - Franco Tagliapietra
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy
| | - Luigi Gallo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy
| | - Paolo Ajmone Marsan
- Department of Animal Science, Food and Nutrition (DIANA) and the Romeo and Enrica Invernizzi Research Center for Sustainable Dairy Production (CREI), Faculty of Agricultural, Food and Environmental Sciences, Catholic University of the Sacred Heart, 29122, Piacenza, Italy; Nutrigenomics and Proteomics Research Center, Catholic University of the Sacred Heart, 29122, Piacenza, Italy
| | - Erminio Trevisi
- Department of Animal Science, Food and Nutrition (DIANA) and the Romeo and Enrica Invernizzi Research Center for Sustainable Dairy Production (CREI), Faculty of Agricultural, Food and Environmental Sciences, Catholic University of the Sacred Heart, 29122, Piacenza, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro (PD), Italy
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Mota LFM, Giannuzzi D, Pegolo S, Trevisi E, Ajmone-Marsan P, Cecchinato A. Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows. Genet Sel Evol 2023; 55:23. [PMID: 37013482 PMCID: PMC10069109 DOI: 10.1186/s12711-023-00795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 03/21/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios. RESULTS The predictive ability of these approaches was measured by the coefficient of determination (R2). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R2 for blood metabolites across the three CV scenarios, especially with the herd-out CV: R2 values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators. CONCLUSIONS Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.
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Affiliation(s)
- Lucio F M Mota
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro, PD, Italy.
| | - Diana Giannuzzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro, PD, Italy
| | - Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro, PD, Italy
| | - Erminio Trevisi
- Department of Animal Science, Food and Nutrition (DIANA) and the Romeo and Enrica Invernizzi Research Center for Sustainable Dairy Production (CREI), Faculty of Agricultural, Food, and Environmental Sciences, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy
- Nutrigenomics and Proteomics Research Center, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy
| | - Paolo Ajmone-Marsan
- Department of Animal Science, Food and Nutrition (DIANA) and the Romeo and Enrica Invernizzi Research Center for Sustainable Dairy Production (CREI), Faculty of Agricultural, Food, and Environmental Sciences, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy
- Nutrigenomics and Proteomics Research Center, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020, Legnaro, PD, Italy
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11
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Nan L, Du C, Fan Y, Liu W, Luo X, Wang H, Ding L, Zhang Y, Chu C, Li C, Ren X, Yu H, Lu S, Zhang S. Association between Days Open and Parity, Calving Season or Milk Spectral Data. Animals (Basel) 2023; 13:ani13030509. [PMID: 36766398 PMCID: PMC9913365 DOI: 10.3390/ani13030509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 02/04/2023] Open
Abstract
Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.
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Affiliation(s)
- Liangkang Nan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chao Du
- Henan Institute of Science and Technology, College of Animal Science and Veterinary Medicine, Xinxiang 453003, China
| | - Yikai Fan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Wenju Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xuelu Luo
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Haitong Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lei Ding
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yi Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chu Chu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chunfang Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoli Ren
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Yu
- Hebei Livestock Breeding Station, Shijiazhuang 050000, China
| | - Shiyu Lu
- Hebei Livestock Breeding Station, Shijiazhuang 050000, China
| | - Shujun Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence:
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12
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Application of Optical Quality Control Technologies in the Dairy Industry: An Overview. PHOTONICS 2021. [DOI: 10.3390/photonics8120551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.
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13
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Walleser E, Mandujano Reyes JF, Anklam K, Höltershinken M, Hertel-Boehnke P, Döpfer D. Developing a predictive model for beta-hydroxybutyrate and non-esterified fatty acids using milk fourier-transform infrared spectroscopy in dairy cows. Prev Vet Med 2021; 197:105509. [PMID: 34678645 DOI: 10.1016/j.prevetmed.2021.105509] [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: 05/24/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
Negative energy balance following parturition predisposes dairy cattle to numerous metabolic disorders. Current diagnostics are limited by their labor requirements and inability to measure multiple metabolic markers simultaneously. Fourier-transform Infrared spectroscopy (FTIR) data, measured from milk samples, could improve the detection of metabolic disorders using routine milk samples from dairy farms. The objective of this study was to develop a predictive model for numeric values of blood beta-hydroxybutyrate (BHB) and blood non-esterified fatty acids (NEFA). The study utilized a dataset comprised of 622 observations with known blood BHB and blood NEFA values measured concurrently with the milk FTIR data. Using ElasticNet regression on milk FTIR data and production information, we built regression models for numeric blood BHB and blood NEFA prediction and classification models for blood BHB values greater than 1.2 mmol/L and blood NEFA values greater than 0.7 mmol/L. The R2 of the best fitting model was 0.56 and 0.51 for log-transformed BHB and log-transformed NEFA, respectively. The BHB classification model had a 90 % sensitivity and 83 % specificity and the NEFA classification model achieved a sensitivity of 73 % and specificity of 74 %. We applied our numeric prediction models to an external dataset (n = 9660) with known blood metabolites to validate the prediction accuracy of log-transformed blood BHB and log-transformed blood NEFA models. Log-transformed BHB root mean square error (RMSE) was 0.4018 and log-transformed NEFA RMSE was 0.4043. The second objective of this study was to develop a categorization for cows as either metabolically disordered or healthy. Responses to negative energy balance in transition cows are related to blood levels of BHB and NEFA. Cows suffering from metabolic disorders without elevated blood BHB values remain unidentified when detection is focused on blood BHB alone. To account for this differentiated metabolic response, we categorized cows as either 'metabolically healthy' or suffering a 'metabolic disorder' by using a combination of blood BHB, blood NEFA, and milk fat to protein quotient. We obtained a balanced accuracy of 94 % for the prediction of cow metabolic status. Direct prediction of metabolic status can be used to identify hyperketonemic cows in addition to cows exhibiting metabolic response patterns not detected by elevated blood BHB alone.
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Affiliation(s)
- E Walleser
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison, 53706, USA.
| | - J F Mandujano Reyes
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison, 53706, USA
| | - K Anklam
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison, 53706, USA
| | - M Höltershinken
- University of Veterinary Medicine Hannover, Foundation Clinic for Cattle, Hannover, Germany
| | - P Hertel-Boehnke
- Bavarian State Research Centre for Agriculture (LfL), Institute for Animal Nutrition and Feed Management, Grub, Germany
| | - D Döpfer
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison, 53706, USA
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14
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Xia X, Weng Y, Zhang L, Tang R, Zhang X. A facile SERS strategy to detect glucose utilizing tandem enzyme activities of Au@Ag nanoparticles. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 259:119889. [PMID: 34015600 DOI: 10.1016/j.saa.2021.119889] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 03/21/2021] [Accepted: 04/25/2021] [Indexed: 06/12/2023]
Abstract
Surface-Enhanced Raman Scattering (SERS) is a powerful analysis technology, attracting more and more attention due to its high sensitivity and selectivity. Herein, we report a simple seed-mediated method to synthesize Au@Ag nanoparticles (NPs) as a multifunctional biosensor for the label-free detection of hydrogen peroxide (H2O2) and glucose by SERS. Au@Ag NPs, as an ultrasensitive SERS substrate, show the dual activities (peroxidase-like and GOx-like activities). Under the condition of pH 4.0 NaAc buffer solution, the glucose and H2O can be catalyzed by Au@Ag NPs to produce glucose acid and H2O2, and then H2O2 can oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to form a blue oxidation product oxidic TMB (oxTMB) which exhibits strong SERS signals at 1188, 1330, 1605 cm-1. Thus, we have developed a new SERS strategy for analysis of glucose with a detection limit of 5 × 10-10molL-1, suggesting that Au@Ag NPs have the potential for biosensor, immunoassay and medical treatment.
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Affiliation(s)
- Xuemin Xia
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Yijin Weng
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Lei Zhang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Ruyi Tang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xia Zhang
- School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
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15
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Milk infrared spectra from multiple instruments improve performance of prediction models. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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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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Bahadi M, Ismail AA, Vasseur E. Fourier Transform Infrared Spectroscopy as a Tool to Study Milk Composition Changes in Dairy Cows Attributed to Housing Modifications to Improve Animal Welfare. Foods 2021; 10:450. [PMID: 33670588 PMCID: PMC7922570 DOI: 10.3390/foods10020450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/10/2021] [Accepted: 02/13/2021] [Indexed: 11/16/2022] Open
Abstract
Animal welfare status is assessed today through visual evaluations requiring an on-farm visit. A convenient alternative would be to detect cow welfare status directly in milk samples, already routinely collected for milk recording. The objective of this study was to propose a novel approach to demonstrate that Fourier transform infrared (FTIR) spectroscopy can detect changes in milk composition related to cows subjected to movement restriction at the tie stall with four tie-rail configurations varying in height and position (TR1, TR2, TR3 and TR4). Milk mid-infrared spectra were collected on weekly basis. Long-term average spectra were calculated for each cow using spectra collected in weeks 8-10 of treatment. Principal component analysis was applied to spectral averages and the scores of principal components (PCs) were tested for treatment effect by mixed modelling. PC7 revealed a significant treatment effect (p = 0.01), particularly for TR3 (configuration with restricted movement) vs. TR1 (recommended configuration) (p = 0.03). The loading spectrum of PC7 revealed high loadings at wavenumbers that could be assigned to biomarkers related to negative energy balance, such as β-hydroxybutyrate, citrate and acetone. This observation suggests that TR3 might have been restrictive for cows to access feed. Milk FTIR spectroscopy showed promising results in detecting welfare status and housing conditions in dairy cows.
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Affiliation(s)
- Mazen Bahadi
- McGill IR Group, McGill University, Sainte Anne de Bellevue, QC H9X 3V9, Canada;
| | - Ashraf A. Ismail
- McGill IR Group, McGill University, Sainte Anne de Bellevue, QC H9X 3V9, Canada;
| | - Elsa Vasseur
- Department of Animal Science, McGill University, Sainte Anne de Bellevue, QC H9X 3V9, Canada;
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18
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Ho PN, Luke TDW, Pryce JE. Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia. J Dairy Sci 2021; 104:4467-4477. [PMID: 33551158 DOI: 10.3168/jds.2020-19603] [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: 09/08/2020] [Accepted: 11/13/2020] [Indexed: 11/19/2022]
Abstract
Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of β-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk mid-infrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R2) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R2 values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R2 remained comparable, however, the root mean squared errors increased substantially (4-10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.
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Affiliation(s)
- P N Ho
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia.
| | - T D W Luke
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - J E Pryce
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
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19
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Proxy Measures and Novel Strategies for Estimating Nitrogen Utilisation Efficiency in Dairy Cattle. Animals (Basel) 2021; 11:ani11020343. [PMID: 33572868 PMCID: PMC7911641 DOI: 10.3390/ani11020343] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/19/2021] [Accepted: 01/26/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Dairy cow diets contain nitrogen, mostly in the form of protein. However, dietary nitrogen is used with a low efficiency for milk production, and much of the unused nitrogen is converted to urea and excreted in urine and faeces (manure). Nitrogen within manure can then be lost to the environment, and this is a particular issue when dairy cows are offered diets containing excess dietary protein. As a result, there is increasing pressure on the dairy sector to improve the efficiency with which dairy cows utilise dietary nitrogen. While nitrogen utilisation efficiency can be measured accurately on research farms, this is more difficult on commercial farms. For that reason, there is much interest in developing low-cost and easy-to-use proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of proximate analyses that are already used as indicators of nitrogen use efficiency in dairy cows (e.g., blood urea and milk urea), and a number of more novel measures that may have potential for use in the future (including analysis of milk, blood, urine, breath, and predictions of intake). These ‘proxy’ measurements can be used to improve feeding management and might be used to monitor adherence to legislation. Abstract The efficiency with which dairy cows convert dietary nitrogen (N) to milk N is generally low (typically 25%). As a result, much of the N consumed is excreted in manure, from which N can be lost to the environment. Therefore there is increasing pressure to reduce N excretion and improve N use efficiency (NUE) on dairy farms. However, assessing N excretion and NUE on farms is difficult, thus the need to develop proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of these proximate measures. While a strong relationship exists between blood urea N and urinary N excretion, blood sampling is an invasive technique unsuitable for regular herd monitoring. Milk urea N (MUN) can be measured non-invasively, and while strong relationships exist between dietary crude protein and MUN, and MUN and urinary N excretion, the technique has limitations. Direct prediction of NUE using mid-infrared analysis of milk has real potential, while techniques such as near-infrared spectroscopy analysis of faeces and manure have received little attention. Similarly, techniques such as nitrogen isotope analysis, nuclear magnetic resonance spectroscopy of urine, and breath ammonia analysis may all offer potential in the future, but much research is still required.
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20
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Grelet C, Dardenne P, Soyeurt H, Fernandez JA, Vanlierde A, Stevens F, Gengler N, Dehareng F. Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions. Methods 2020; 186:97-111. [PMID: 32763376 DOI: 10.1016/j.ymeth.2020.07.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 06/12/2020] [Accepted: 07/27/2020] [Indexed: 12/17/2022] Open
Abstract
Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The second objective is to emphasize methods and indicators to evaluate the quality of models and the quality of predictions generated under routine conditions. The last objective is to outline the issues and the solutions linked with the use and transfer of models on large number of instruments. Based on partial least square regression and 10 datasets including milk MIR spectra and reference quantitative values for 57 traits of interest, the impact of the different factors is illustrated by evaluating the influence on the validation root mean square error of prediction (RMSEP). In the displayed examples, all factors, when well set up, increase the quality of predictions, with an improvement of the RMSEP ranging from 12% to 43%. This work also aims to underline the need for and the complementarity between different validation procedures, statistical parameters and quality assurance methods. Finally, when using and transferring models, the impact of the spectral standardization on the prediction reproducibility is highlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions.
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Affiliation(s)
- C Grelet
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - P Dardenne
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - H Soyeurt
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.
| | - J A Fernandez
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - A Vanlierde
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - F Stevens
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
| | - N Gengler
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.
| | - F Dehareng
- Walloon Agricultural Research Center (CRA-W), 24 Chaussée de Namur, 5030 Gembloux, Belgium.
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21
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Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques. SENSORS 2020; 20:s20154238. [PMID: 32751425 PMCID: PMC7435658 DOI: 10.3390/s20154238] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 07/21/2020] [Accepted: 07/27/2020] [Indexed: 12/03/2022]
Abstract
In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.
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22
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Aernouts B, Adriaens I, Diaz-Olivares J, Saeys W, Mäntysaari P, Kokkonen T, Mehtiö T, Kajava S, Lidauer P, Lidauer MH, Pastell M. Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows. J Dairy Sci 2020; 103:6422-6438. [PMID: 32389474 DOI: 10.3168/jds.2019-17952] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 02/29/2020] [Indexed: 11/19/2022]
Abstract
In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations <0.6 mmol/L with a root mean square error of prediction of <0.143 mmol/L. However, for blood plasma with >1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping.
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Affiliation(s)
- Ben Aernouts
- KU Leuven, Department of Biosystems, Biosystems Technology Cluster, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium; KU Leuven, Department of Biosystems, Mechatronics, Biostatistics and Sensors Division, Kasteelpark Arenberg 30, 3001 Leuven, Belgium; Natural Resources Institute of Finland (Luke), Maarintie 6, 02150 Espoo, Finland.
| | - Ines Adriaens
- KU Leuven, Department of Biosystems, Biosystems Technology Cluster, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium; KU Leuven, Department of Biosystems, Mechatronics, Biostatistics and Sensors Division, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
| | - José Diaz-Olivares
- KU Leuven, Department of Biosystems, Biosystems Technology Cluster, Campus Geel, Kleinhoefstraat 4, 2440 Geel, Belgium; KU Leuven, Department of Biosystems, Mechatronics, Biostatistics and Sensors Division, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
| | - Wouter Saeys
- KU Leuven, Department of Biosystems, Mechatronics, Biostatistics and Sensors Division, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
| | - Päivi Mäntysaari
- Natural Resources Institute of Finland (Luke), Tietotie 4, 31600 Jokioinen, Finland
| | - Tuomo Kokkonen
- University of Helsinki, Department of Agricultural Sciences, Koetilantie 5, 00014 Helsinki, Finland
| | - Terhi Mehtiö
- Natural Resources Institute of Finland (Luke), Tietotie 4, 31600 Jokioinen, Finland
| | - Sari Kajava
- Natural Resources Institute of Finland (Luke), Halolantie 31 A, 71750 Maaninka, Finland
| | - Paula Lidauer
- Natural Resources Institute of Finland (Luke), Tietotie 4, 31600 Jokioinen, Finland
| | - Martin H Lidauer
- Natural Resources Institute of Finland (Luke), Tietotie 4, 31600 Jokioinen, Finland
| | - Matti Pastell
- Natural Resources Institute of Finland (Luke), Maarintie 6, 02150 Espoo, Finland
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23
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Benedet A, Costa A, De Marchi M, Penasa M. Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows. J Dairy Sci 2020; 103:6354-6363. [PMID: 32359995 DOI: 10.3168/jds.2019-17916] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 02/24/2020] [Indexed: 11/19/2022]
Abstract
At the beginning of lactation, high-producing cows commonly experience an unbalanced energy status that is often responsible for the onset of metabolic disorders and impaired health and performance. Blood β-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) are indicators of excessive fat mobilization and circulating ketone bodies. Recently, prediction models based on mid-infrared (MIR) spectroscopy have been developed to assess blood BHB and NEFA from routinely collected individual milk samples. This study aimed to estimate genetic parameters of blood BHB and NEFA predicted from milk MIR spectra and to assess their phenotypic and genetic correlations with milk production and composition traits in early-lactation Holstein cows. The data set comprised the first test-day record within lactation and spectra of individual milk samples (n = 22,718) of 13,106 Holstein cows collected from 5 to 35 d in milk (DIM). Blood BHB and NEFA were predicted from milk MIR spectra using previously developed prediction models. Genetic parameters of blood metabolites and milk traits were estimated for the whole observational period (5-35 DIM) and within 6 classes of DIM. Blood BHB and NEFA showed similar genetic variation across DIM, with the highest heritability in the first 10 d after calving (0.31 ± 0.06 and 0.19 ± 0.05 for BHB and NEFA, respectively). The genetic correlation between BHB and NEFA was moderate (0.51 ± 0.05). Genetic correlations of BHB with milk yield, SCS, protein percentage, lactose percentage, and urea nitrogen content were similar to, or at least in the same direction as, the correlations of NEFA with the same traits, whereas opposite correlations were observed with fat percentage and fat-to-protein ratio. Results of the current study suggest that blood BHB and NEFA predicted from milk MIR spectra have genetic variation that is potentially exploitable for breeding purposes. Therefore, they could be used as indicator traits of hyperketonemia in a selection index aimed to reduce the susceptibility of dairy cows to metabolic disorders in early lactation.
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Affiliation(s)
- A Benedet
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - A Costa
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.
| | - M De Marchi
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
| | - M Penasa
- Department of Agronomy, Food, Natural resources, Animals and Environment, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
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24
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Foldager L, Gaillard C, Sorensen MT, Larsen T, Matthews E, O'Flaherty R, Carter F, Crowe MA, Grelet C, Salavati M, Hostens M, Ingvartsen KL, Krogh MA. Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers. Prev Vet Med 2020; 179:105006. [PMID: 32361640 DOI: 10.1016/j.prevetmed.2020.105006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 04/10/2020] [Accepted: 04/11/2020] [Indexed: 11/25/2022]
Abstract
Blood biomarkers may be used to detect physiological imbalance and potential disease. However, blood sampling is difficult and expensive, and not applicable in commercial settings. Instead, individual milk samples are readily available at low cost, can be sampled easily and analysed instantly. The present observational study sampled blood and milk from 234 Holstein dairy cows from experimental herds in six European countries. The objective was to compare the use of three different sets of milk biomarkers for identification of cows in physiological imbalance and thus at risk of developing metabolic or infectious diseases. Random forests was used to predict body energy balance (EBAL), index for physiological imbalance (PI-index) and three clusters differentiating the metabolic status of cows created on basis of concentrations of plasma glucose, β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA) and serum IGF-1. These three metabolic clusters were interpreted as cows in balance, physiological imbalance and "intermediate cows" with physiological status in between. The three sets of milk biomarkers used for prediction were: milk Fourier transform mid-IR (FT-MIR) spectra, 19 immunoglobulin G (IgG) N-glycans and 8 milk metabolites and enzymes (MME). Blood biomarkers were sampled twice; around 14 days after calving (days in milk (DIM)) and around 35 DIM. MME and FT-MIR were sampled twice weekly 1-50 DIM whereas IgG N-glycan were measured only four times. Performances of EBAL and PI-index predictions were measured by coefficient of determination (R2cv) and root mean squared error (RMSEcv) from leave-one-cow-out cross-validation (cv). For metabolic clusters, performance was measured by sensitivity, specificity and global accuracy from this cross-validation. Best prediction of PI-index was obtained by MME (R2cv = 0.40 (95 % CI: 0.29-0.50) at 14 DIM and 0.35 (0.23-0.44) at 35 DIM) while FT-MIR showed a better performance than MME for prediction of EBAL (R2cv = 0.28 (0.24-0.33) vs 0.21 (0.18-0.25)). Global accuracies of predicting metabolic clusters from MME and FT-MIR were at the same level ranging from 0.54 (95 % CI: 0.39-0.68) to 0.65 (0.55-0.75) for MME and 0.51 (0.37-0.65) to 0.68 (0.53-0.81) for FT-MIR. R2cv and accuracies were lower for IgG N-glycans. In conclusion, neither EBAL nor PI-index were sufficiently well predicted to be used as a management tool for identification of risk cows. MME and FT-MIR may be used to predict the physiological status of the cows, while the use of IgG N-glycans for prediction still needs development. Nevertheless, accuracies need to be improved and a larger training data set is warranted.
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Affiliation(s)
- Leslie Foldager
- Department of Animal Science, Aarhus University, Blichers Allé 20, DK8830, Tjele, Denmark; Bioinformatics Research Centre, Aarhus University, C.F. Møllers Allé 8, DK8000, Aarhus, Denmark.
| | - Charlotte Gaillard
- Department of Animal Science, Aarhus University, Blichers Allé 20, DK8830, Tjele, Denmark
| | - Martin T Sorensen
- Department of Animal Science, Aarhus University, Blichers Allé 20, DK8830, Tjele, Denmark
| | - Torben Larsen
- Department of Animal Science, Aarhus University, Blichers Allé 20, DK8830, Tjele, Denmark
| | | | - Roisin O'Flaherty
- NIBRT GlycoScience Group, National Institute for Bioprocessing, Research and Training, Mount Merrion, Blackrock, Co., Dublin, Ireland
| | - Fiona Carter
- University College Dublin (UCD), Dublin, Ireland
| | - Mark A Crowe
- University College Dublin (UCD), Dublin, Ireland
| | - Clément Grelet
- Walloon Agricultural Research Center (CRA-W), 5030, Gembloux, Belgium
| | | | - Miel Hostens
- Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820, Merelbeke, Belgium
| | - Klaus L Ingvartsen
- Department of Animal Science, Aarhus University, Blichers Allé 20, DK8830, Tjele, Denmark
| | - Mogens A Krogh
- Department of Animal Science, Aarhus University, Blichers Allé 20, DK8830, Tjele, Denmark
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Repeatability and Reproducibility of Measures of Bovine Methane Emissions Recorded Using a Laser Detector. Animals (Basel) 2020; 10:ani10040606. [PMID: 32244846 PMCID: PMC7222733 DOI: 10.3390/ani10040606] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 03/26/2020] [Accepted: 03/30/2020] [Indexed: 11/17/2022] Open
Abstract
Simple Summary The collection of phenotypes related to livestock methane emissions is hampered by costly and time-demanding techniques. In the present research, a laser methane detector was used to measure several novel phenotypes, including mean and aggregate of methane records, and mean and number of methane peak records, considering Simmental heifers as a case study. Phenotypes showed satisfactory repeatability and reproducibility for log-transformed data. The number of emission peaks had great variability across animals and thus it is a promising candidate to discriminate between high and low emitters. Abstract Methane (CH4) emissions represent a worldwide problem due to their direct involvement in atmospheric warming and climate change. Ruminants are among the major players in the global scenario of CH4 emissions, and CH4 emissions are a problem for feed efficiency since enteric CH4 is eructed to the detriment of milk and meat production. The collection of CH4 phenotypes at the population level is still hampered by costly and time-demanding techniques. In the present study, a laser methane detector was used to assess repeatability and reproducibility of CH4 phenotypes, including mean and aggregate of CH4 records, slope of the linear equation modelling the aggregate function, and mean and number of CH4 peak records. Five repeated measurements were performed in a commercial farm on three Simmental heifers, and the same protocol was repeated over a period of three days. Methane emission phenotypes expressed as parts per million per linear meter (ppm × m) were not normally distributed and, thus, they were log-transformed to reach normality. Repeatability and reproducibility were calculated as the relative standard deviation of five measurements within the same day and 15 measurements across three days, respectively. All phenotypes showed higher repeatability and reproducibility for log-transformed data compared with data expressed as ppm × m. The linear equation modelling the aggregate function highlighted a very high coefficient of determination (≥0.99), which suggests that daily CH4 emissions might be derived using this approach. The number of CH4 peaks resulted as particularly diverse across animals and therefore it is a potential candidate to discriminate between high and low emitting animals. Results of this study suggest that laser methane detector is a promising tool to measure bovine CH4 emissions in field conditions.
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26
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Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows. Animals (Basel) 2020; 10:ani10020271. [PMID: 32050647 PMCID: PMC7070724 DOI: 10.3390/ani10020271] [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: 12/20/2019] [Revised: 01/31/2020] [Accepted: 02/07/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Population-level phenotyping of blood metabolites is hardly achievable due to the limitation of reference analyses. Mid-infrared spectroscopy has recently been used to develop prediction models for major blood metabolites, allowing their determination on a large scale. The current study investigated the variation of blood β-hydroxybutyrate, non-esterified fatty acids, and urea nitrogen predicted from a large milk mid-infrared spectra database of Brown Swiss, Holstein-Friesian, and Simmental cows. Holstein-Friesian cows had the greatest concentrations of β-hydroxybutyrate and non-esterified fatty acids, and the lowest urea nitrogen in blood, which may underline an altered energy and nutritional status. Abstract Serum metabolic profile is a common method to monitor health and nutritional status of dairy cows, but blood sampling and analysis are invasive, time-consuming, and expensive. Milk mid-infrared spectra have recently been used to develop prediction models for blood metabolites. The current study aimed to investigate factors affecting blood β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), and urea nitrogen (BUN) predicted from a large milk mid-infrared spectra database. Data consisted of the first test-day record of early-lactation cows in multi-breed herds. Holstein-Friesian cows had the greatest concentration of blood BHB and NEFA, followed by Simmental and Brown Swiss. The greatest and the lowest concentrations of BUN were detected for Brown Swiss and Holstein-Friesian, respectively. The greatest BHB concentration was observed in the first two weeks of lactation for Brown Swiss and Holstein-Friesian. Across the first month of lactation, NEFA decreased and BUN increased for all considered breeds. The greatest concentrations of blood BHB and NEFA were recorded in spring and early summer, whereas BUN peaked in December. Environmental effects identified in the present study can be included as adjusting factors in within-breed estimation of genetic parameters for major blood metabolites.
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