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Huot F, Claveau S, Bunel A, Warner D, Santschi DE, Gervais R, Paquet ER. Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds. J Dairy Sci 2024:S0022-0302(24)00984-6. [PMID: 38971559 DOI: 10.3168/jds.2024-25034] [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: 04/10/2024] [Accepted: 06/08/2024] [Indexed: 07/08/2024]
Abstract
Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which the diagnostic remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds. A first data set composed of 488 milk samples of 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, else SARA-) was used for the development of machine learning models. Three sets of predictive variables: i) milk major components (MMC), ii) milk FA (MFA), and iii) MMC combined with MFA (MMCFA) were submitted to 3 different algorithms, namely Elastic net (EN), Extreme gradient boosting (XGB), and Partial least squares (PLS), and evaluated using 3 different scenarios of cross-validation. Accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including fatty acids estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, one model was selected (algorithm: EN; predictive variables: MMCFA; 60.4, 65.4, and 55.3% of accuracy, sensitivity, and specificity, respectively) and applied to a large data set comprising the first test-day record (milk major components and FA within the first 70 DIM of 211,972 Holstein cows (219,503 samples) collected from 3001 commercial dairy herds. Based on this analysis, the within-herd SARA prevalence of commercial farms was estimated at 6.6 ± 5.29% ranging from 0 to 38.3%. A subsequent linear mixed model was built to investigate the herd-level factors associated to higher within-herd SARA prevalence. Milking system, proportion of primiparous cows, herd size and seasons were all herd-level factors affecting SARA prevalence. Furthermore, milk production was positively, and milk fat yield negatively associated with SARA prevalence. Due to their moderate levels of accuracy, the SARA prediction models developed in our study, using data from continuous pH measurements on commercial farms, are not suitable for diagnostic purpose. However, these models can provide valuable information at the herd level.
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Affiliation(s)
- F Huot
- Département des sciences animales, Université Laval, Québec, QC, G1V 0A6, Canada; Institut intelligence et données, Université Laval, Québec, QC, G1V 0A6, Canada; Centre de recherche en données massives, Université Laval, Québec, G1V 0A6, Canada
| | | | - A Bunel
- Agrinova, Alma, QC, G8B 7S8, Canada
| | - D Warner
- Lactanet, Ste-Anne-de-Bellevue, QC, H9X 3R4, Canada
| | - D E Santschi
- Lactanet, Ste-Anne-de-Bellevue, QC, H9X 3R4, Canada
| | - R Gervais
- Département des sciences animales, Université Laval, Québec, QC, G1V 0A6, Canada.
| | - E R Paquet
- Département des sciences animales, Université Laval, Québec, QC, G1V 0A6, Canada; Institut intelligence et données, Université Laval, Québec, QC, G1V 0A6, Canada; Centre de recherche en données massives, Université Laval, Québec, G1V 0A6, Canada.
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Fresco S, Vanlierde A, Boichard D, Lefebvre R, Gaborit M, Bore R, Fritz S, Gengler N, Martin P. Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra. Animal 2024; 18:101200. [PMID: 38870588 DOI: 10.1016/j.animal.2024.101200] [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: 10/03/2023] [Revised: 05/11/2024] [Accepted: 05/14/2024] [Indexed: 06/15/2024] Open
Abstract
Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.
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Affiliation(s)
- S Fresco
- Eliance, 149 rue de Bercy, 75595 Paris cedex 12, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France.
| | - A Vanlierde
- Walloon Agricultural Research Centre, Animal Production Unit, 5030 Gembloux, Belgium
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - R Lefebvre
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - M Gaborit
- INRAE UE326 Domaine Expérimental du Pin, 61310 Exmes, France
| | - R Bore
- Institut de l'Élevage, 149 Rue de Bercy, 75012 Paris, France
| | - S Fritz
- Eliance, 149 rue de Bercy, 75595 Paris cedex 12, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - N Gengler
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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3
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McParland S, Frizzarin M, Lahart B, Kennedy M, Shalloo L, Egan M, Starsmore K, Berry DP. Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples. J Dairy Sci 2024; 107:978-991. [PMID: 37709036 DOI: 10.3168/jds.2023-23577] [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: 04/04/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.
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Affiliation(s)
- S McParland
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - M Frizzarin
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - B Lahart
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - M Kennedy
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - L Shalloo
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - M Egan
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - K Starsmore
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland
| | - D P Berry
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy P61 P302, Co. Cork, Ireland.
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4
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Lopdell TJ. Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle. Animals (Basel) 2023; 13:ani13050911. [PMID: 36899768 PMCID: PMC10000085 DOI: 10.3390/ani13050911] [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: 01/17/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.
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Villar-Hernández BDJ, Amalfitano N, Cecchinato A, Pazzola M, Vacca GM, Bittante G. Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats. Foods 2023; 12:foods12040807. [PMID: 36832882 PMCID: PMC9955890 DOI: 10.3390/foods12040807] [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: 01/18/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm-1), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.
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Affiliation(s)
| | - Nicolò Amalfitano
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
| | - Michele Pazzola
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | | | - Giovanni Bittante
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
- Correspondence:
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6
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Coppa M, Vanlierde A, Bouchon M, Jurquet J, Musati M, Dehareng F, Martin C. Methodological guidelines: Cow milk mid-infrared spectra to predict GreenFeed enteric methane emissions. J Dairy Sci 2022; 105:9271-9285. [PMID: 36175234 DOI: 10.3168/jds.2022-21890] [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: 01/28/2022] [Accepted: 06/29/2022] [Indexed: 11/19/2022]
Abstract
Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH4) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH4 emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH4 spot measurements with GF were taken as the basic measurement unit (BMU) of CH4. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH4 reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH4 measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH4 measurement by GF performed better than short duration: the coefficient of determination of validation (R2V) for CH4 emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH4 emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH4 measurement period gave better predictions than using day spectra (R2V = 0.70 vs. 0.60 for CH4 as g/d on 4 BMU). Correcting the day spectra by DIM improved R2V compared with the equivalent DIM-uncorrected models (R2V = 0.67 vs. 0.60 for CH4 as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH4 measurement period (R2V = 0.73 vs. 0.70 for CH4 as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R2V = 0.13 for CH4 as g/d on 1 BMU) or with (R2V = 0.31 for CH4 as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH4 prediction when cows receive chemical inhibitors of CH4 emissions not affecting milk composition.
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Affiliation(s)
- M Coppa
- Independent researcher, Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France
| | - A Vanlierde
- Walloon Agricultural Research Centre, B-5030 Gembloux, Belgium
| | - M Bouchon
- INRAE, UE1414 Herbipôle, 63122 Saint-Genès-Champanelle, France
| | - J Jurquet
- Institut de l'Elevage, 42 rue Georges Morel CS 60057, 49071 Beaucouzé cedex, France
| | - M Musati
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France; Department Di3A, University of Catania, via Valdisavoia 5, 95123 Catania, Italy
| | - F Dehareng
- Walloon Agricultural Research Centre, B-5030 Gembloux, Belgium
| | - C Martin
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France.
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Shadpour S, Chud TC, Hailemariam D, Plastow G, Oliveira HR, Stothard P, Lassen J, Miglior F, Baes CF, Tulpan D, Schenkel FS. Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. J Dairy Sci 2022; 105:8272-8285. [DOI: 10.3168/jds.2021-21176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 06/09/2022] [Indexed: 11/19/2022]
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8
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Liu R, Hailemariam D, Yang T, Miglior F, Schenkel F, Wang Z, Stothard P, Zhang S, Plastow G. Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system. Animal 2022; 16:100469. [PMID: 35190321 DOI: 10.1016/j.animal.2022.100469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 12/01/2022] Open
Abstract
Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R2val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R2val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R2val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R2val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.
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Affiliation(s)
- R Liu
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada; Key Laboratory of Animal Breeding and Reproduction of Ministry of Education, Hauzhong Agricultural University, Wuhan 430070, China
| | - D Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada.
| | - T Yang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
| | - F Miglior
- Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
| | - F Schenkel
- Center for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
| | - Z Wang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
| | - P Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
| | - S Zhang
- Key Laboratory of Animal Breeding and Reproduction of Ministry of Education, Hauzhong Agricultural University, Wuhan 430070, China
| | - G Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton T6G 2R3, Canada
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Khanal P, Tempelman RJ. The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows. J Dairy Sci 2022; 105:3209-3221. [DOI: 10.3168/jds.2021-21079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/21/2021] [Indexed: 11/19/2022]
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10
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Richardson C, Amer P, Quinton C, Crowley J, Hely F, van den Berg I, Pryce J. Reducing greenhouse gas emissions through genetic selection in the Australian dairy industry. J Dairy Sci 2022; 105:4272-4288. [DOI: 10.3168/jds.2021-21277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 12/22/2021] [Indexed: 11/19/2022]
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11
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In Vitro and In Vivo Evaluation of the Effects of a Compound Based on Plants, Yeast and Trace Elements on the Ruminal Function of Dairy Cows. DAIRY 2021. [DOI: 10.3390/dairy2040043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The high production levels reached by the dairy sector need adjustment in nutritional inputs and efficient feed conversion. In this context, we evaluated a compound (QY—Qualix Yellow) combining optimized inputs in trace elements and 20% MIX 3.0. In a first step, the effects of MIX 3.0 on ruminal function were assessed in vitro by incubating ruminal fluid with the mixture at a ratio of 20:1. The results obtained encouraged us to test QY in vivo, on a herd of dairy cows. The herd was divided into one group of 19 dairy cows receiving the compound and a control group of 20 animals conducted in the same conditions, but which did not received the compound; the production performance and feed efficiency of the two groups were compared. In vitro experiments showed improved digestion of acid and neutral detergent fibres by 10%. The propionate production was enhanced by 14.5% after 6 h incubation with MIX 3.0. The plant mixture decreased the production of methane and ammonia by 37% and 52%, respectively, and reduced the number of protozoa by 50%. An increase in milk yield by 2.4 kg/cow/d (p < 0.1), combined with a decrease in concentrate consumption of 0.27 kg DM/cow/d (p < 0.001), was observed in vivo after consumption of the compound. Sixty-six days after the beginning of the trial, methane emissions per kg of milk were significantly lower in the group receiving QY. In conclusion, MIX 3.0 induced change in ruminal function in vitro and, when it entered into the composition of the QY, it appeared to improve feed efficiency and production performance in vivo.
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12
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Tiplady KM, Lopdell TJ, Reynolds E, Sherlock RG, Keehan M, Johnson TJJ, Pryce JE, Davis SR, Spelman RJ, Harris BL, Garrick DJ, Littlejohn MD. Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle. Genet Sel Evol 2021; 53:62. [PMID: 34284721 PMCID: PMC8290608 DOI: 10.1186/s12711-021-00648-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 06/22/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk. RESULTS Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk. CONCLUSIONS This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.
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Affiliation(s)
- Kathryn M. Tiplady
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - Thomas J. Lopdell
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Edwardo Reynolds
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - Richard G. Sherlock
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Michael Keehan
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Thomas JJ. Johnson
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Jennie E. Pryce
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Stephen R. Davis
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Richard J. Spelman
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Bevin L. Harris
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - Dorian J. Garrick
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - Mathew D. Littlejohn
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
- School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
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13
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Vanlierde A, Dehareng F, Gengler N, Froidmont E, McParland S, Kreuzer M, Bell M, Lund P, Martin C, Kuhla B, Soyeurt H. Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid-infrared spectra. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3394-3403. [PMID: 33222175 DOI: 10.1002/jsfa.10969] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 11/10/2020] [Accepted: 11/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND A robust proxy for estimating methane (CH4 ) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid-infrared (FT-MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d-1 ) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT-MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d-1 ) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross-validation statistics: R2 = 0.68 and standard error = 57 g CH4 d-1 ). CONCLUSIONS The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large-scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Amélie Vanlierde
- Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre, Gembloux, Belgium
| | - Frédéric Dehareng
- Knowledge and valorization of agricultural products Department, Walloon Agricultural Research Centre, Gembloux, Belgium
| | - Nicolas Gengler
- AGROBIOCHEM Department and Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Eric Froidmont
- Productions in agriculture Department, Walloon Agricultural Research Centre, Gembloux, Belgium
| | - Sinead McParland
- Department of Animal & Grassland, Moorepark Research and Innovation Centre, Teagasc - The Agriculture and Food Development Authority, Ireland
| | - Michael Kreuzer
- Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zürich, Switzerland
| | - Matthew Bell
- Agri-Food and Biosciences Institute (AFBI), Hillsborough, UK
| | - Peter Lund
- Department of Animal Science, Aarhus University, AU Foulum, Tjele, Denmark
| | - Cécile Martin
- UMR Herbivores, Centre de Recherches Clermont Auvergne-Rhône-Alpes - INRAe - Site de Theix, Saint-Genès-Champanelle, France
| | - Björn Kuhla
- Institute of Nutritional Physiology Oskar Kellner', Leibniz Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
| | - Hélène Soyeurt
- AGROBIOCHEM Department and Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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Appropriate Data Quality Checks Improve the Reliability of Values Predicted from Milk Mid-Infrared Spectra. Animals (Basel) 2021; 11:ani11020533. [PMID: 33670810 PMCID: PMC7922538 DOI: 10.3390/ani11020533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/14/2021] [Indexed: 11/23/2022] Open
Abstract
Simple Summary There is a growing interest in using milk mid-infrared (MIR) spectrometry to obtain new phenotypes to assist in the complex management of dairy farms. These predictive values can be erroneous for many reasons, even if the prediction equations used are accurate. Unfortunately, there is no quality protocol routinely implemented to detect those abnormal predictive values in the database recorded by dairy herd improvement (DHI) organizations, except for fat and protein contents. However, for financial and practical reasons, it is unfeasible to adapt the quality protocol commonly used in milk laboratories to improve the accuracy of those traits. So, this study proposes three different statistical methods that would be easy to implement by DHI organizations to detect abnormal values and limit the spectral extrapolation in order to improve the accuracy of MIR-based predictive values. Abstract The use of abnormal milk mid-infrared (MIR) spectrum strongly affects prediction quality, even if the prediction equations used are accurate. So, this record must be detected after or before the prediction process to avoid erroneous spectral extrapolation or the use of poor-quality spectral data by dairy herd improvement (DHI) organizations. For financial or practical reasons, adapting the quality protocol currently used to improve the accuracy of fat and protein contents is unfeasible. This study proposed three different statistical methods that would be easy to implement by DHI organizations to solve this issue: the deletion of 1% of the extreme high and low predictive values (M1), the deletion of records based on the Global-H (GH) distance (M2), and the deletion of records based on the absolute fat residual value (M3). Additionally, the combinations of these three methods were investigated. A total of 346,818 milk samples were analyzed by MIR spectrometry to predict the contents of fat, protein, and fatty acids. Then, the same traits were also predicted externally using their corresponded standardized MIR spectra. The interest in cleaning procedures was assessed by estimating the root mean square differences (RMSDs) between those internal and external predicted phenotypes. All methods allowed for a decrease in the RMSD, with a gain ranging from 0.32% to 41.39%. Based on the obtained results, the “M1 and M2” combination should be preferred to be more parsimonious in the data loss, as it had the higher ratio of RMSD gain to data loss. This method deleted the records based on the 2% extreme predictions and a GH threshold set at 5. However, to ensure the lowest RMSD, the “M2 or M3” combination, considering a GH threshold of 5 and an absolute fat residual difference set at 0.30 g/dL of milk, was the most relevant. Both combinations involved M2 confirming the high interest of calculating the GH distance for all samples to predict. However, if it is impossible to estimate the GH distance due to a lack of relevant information to compute this statistical parameter, the obtained results recommended the use of M1 combined with M3. The limitation used in M3 must be adapted by the DHI, as this will depend on the spectral data and the equation used. The methodology proposed in this study can be generalized for other MIR-based phenotypes.
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15
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Mensching A, Zschiesche M, Hummel J, Grelet C, Gengler N, Dänicke S, Sharifi AR. Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows. J Dairy Sci 2021; 104:4615-4634. [PMID: 33589252 DOI: 10.3168/jds.2020-19516] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 11/10/2020] [Indexed: 11/19/2022]
Abstract
A routine monitoring for subacute ruminal acidosis (SARA) on the individual level could support the minimization of economic losses and the ensuring of animal welfare in dairy cows. The objectives of this study were (1) to develop a SARA risk score (SRS) by combining information from different data acquisition systems to generate an integrative indicator trait, (2) the investigation of associations of the SRS with feed analysis data, blood characteristics, performance data, and milk composition, including the fatty acid (FA) profile, (3) the development of a milk mid-infrared (MIR) spectra-based prediction equation for this novel reference trait SRS, and (4) its application to an external data set consisting of MIR data of test day records to investigate the association between the MIR-based predictions of the SRS and the milk FA profile. The primary data set, which was used for the objectives (1) to (3), consisted of data collected from 10 commercial farms with a total of 100 Holstein cows in early lactation. The data comprised barn climate parameters, pH and temperature logging from intrareticular measurement boluses, as well as jaw movement and locomotion behavior recordings of noseband-sensor halters and pedometers. Further sampling and data collection included feed samples, blood samples, milk performance, and milk samples, whereof the latter were used to get the milk MIR spectra and to estimate the main milk components, the milk FA profile, and the lactoferrin content. Because all measurements were characterized by different temporal resolutions, the data preparation consisted of an aggregation into values on a daily basis and merging it into one data set. For the development of the SRS, a total of 7 traits were selected, which were derived from measurements of pH and temperature in the reticulum, chewing behavior, and milk yield. After adjustment for fixed effects and standardization, these 7 traits were combined into the SRS using a linear combination and directional weights based on current knowledge derived from literature studies. The secondary data set was used for objective (4) and consisted of test day records of the entire herds, including performance data, milk MIR spectra and MIR-predicted FA. At farm level, it could be shown that diets with higher proportions of concentrated feed resulted in both lower daily mean pH and higher SRS values. On the individual level, an increased SRS could be associated with a modified FA profile (e.g., lower levels of short- and medium-chain FA, higher levels of C17:0, odd- and branched-chain FA). Furthermore, a milk MIR-based partial least squares regression model with a moderate predictability was established for the SRS. This work provides the basis for the development of routine SARA monitoring and demonstrates the high potential of milk composition-based assessment of the health status of lactating cows.
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Affiliation(s)
- A Mensching
- Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, 37075 Goettingen, Germany; Center for Integrated Breeding Research, University of Goettingen, 37075 Goettingen, Germany.
| | - M Zschiesche
- Ruminant Nutrition Group, Department of Animal Sciences, University of Goettingen, 37077 Goettingen, Germany
| | - J Hummel
- Ruminant Nutrition Group, Department of Animal Sciences, University of Goettingen, 37077 Goettingen, Germany
| | - C Grelet
- Walloon Agricultural Research Center, Knowledge and Valorization of Agricultural Products Department, 5030 Gembloux, Belgium
| | - N Gengler
- TERRA Research and Training Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - S Dänicke
- Institute of Animal Nutrition, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, 38116 Brunswick, Germany
| | - A R Sharifi
- Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, 37075 Goettingen, Germany; Center for Integrated Breeding Research, University of Goettingen, 37075 Goettingen, Germany
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16
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Coppa M, Martin B, Hulin S, Guillemin J, Gauzentes JV, Pecou A, Andueza D. Prediction of indicators of cow diet composition and authentication of feeding specifications of Protected Designation of Origin cheese using mid-infrared spectroscopy on milk. J Dairy Sci 2020; 104:112-125. [PMID: 33162089 DOI: 10.3168/jds.2020-18468] [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: 03/04/2020] [Accepted: 08/17/2020] [Indexed: 11/19/2022]
Abstract
The ability of mid-infrared spectroscopy (MIR) to predict indicators (1) of diet composition in dairy herds and (2) for the authentication of the cow feeding restrictions included in the specification of 2 Protected Designation of Origin (PDO) cheeses (Cantal and Laguiole) was tested on 7,607 bulk milk spectra from 1,355 farms located in the Massif Central area of France. For each milk sample, the corresponding cow diet composition data were obtained through on-farm surveys. The cow diet compositions varied largely (i.e., from full grazing for extensive farming systems to corn silage-based diets, which are typical of more intensive farming systems). Partial least square regression and discriminant analysis were used to predict the proportion of different feedstuffs in the cows' diets and to authenticate the cow feeding restrictions for the PDO cheese specifications, respectively. The groups for the discriminant analysis were created by dividing the data set according to the threshold of a specific feedstuff. They were issued based on the specifications of the restriction of the PDO cheese. The pasture proportion in the cows' diets was predicted by MIR with an coefficient of determination in external validation (R2V) = 0.81 and a standard error of prediction of 11.7% dry matter. Pasture + hay, corn silage, conserved herbage, fermented forage, and total herbage proportion in the cows' diets were predicted with a R2V >0.61 and a standard error of prediction <14.8. The discrimination models for pasture presence, pasture ≥50%, and pasture ≥57% in the cows' diets achieved an accuracy and specificity ≥90%. A sensitivity and precision ≥85% were also observed for the pasture proportion discrimination models, but both of these indexes decreased at increasing thresholds from 0 to 50, and 57% pasture in the cows' diets. An accuracy ≥80% was also observed for pasture + hay ≥72%, herbage ≥50%, pasture + hay ≥25%, absence of fermented herbage, absence of corn silage, and corn silage ≤30% in the cows' diets, but for several models, either the sensitivity or precision was lower than the accuracy. Models built on the simultaneous respect of all the criteria of the feeding restrictions of PDO cheese specifications achieved an accuracy, specificity, sensitivity, and precision >90%. Both the regression and discriminant MIR models for bulk milk can provide useful indicators of cow diet composition and PDO cheese specifications to producers and consumers (farmers, dairy plants).
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Affiliation(s)
- M Coppa
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France.
| | - B Martin
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France
| | - S Hulin
- Pôle Fromager AOP Massif Central, 20 Côte de Reyne, F-15000 Aurillac, France
| | - J Guillemin
- Cantal Conseil Elevage, 26 Rue du 139ème Régiment d'Infanterie-BP 239, F-15002 Aurillac
| | | | - A Pecou
- Centre National Interprofessionnel de l'Economie Laitière (CNIEL), 42 Rue de Châteaudun I, F-75314 Paris, France
| | - D Andueza
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 1213 Herbivores, F-63122 Saint-Genès-Champanelle, France
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17
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Bittante G, Cipolat-Gotet C, Cecchinato A. Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle. Animals (Basel) 2020; 10:ani10091654. [PMID: 32942618 PMCID: PMC7552146 DOI: 10.3390/ani10091654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/06/2020] [Accepted: 09/11/2020] [Indexed: 01/20/2023] Open
Abstract
This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH4/DM: 0.232-0.317) and methane intensity per kg of corrected milk (CH4/CM: 0.177-0.279), smaller per kg cheese solids (CH4/SO: 0.093-0.165), but greater per kg fresh cheese (CH4/CU: 0.203-0.267) and for methane production (dCH4: 0.195-0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH4/DM (0.45-0.60), and very variable for dCH4 according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH4/CM, CH4/CU and dCH4, could be considered in breeding programs aimed at the improvement of milk ecological footprint.
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Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 1, 35020 Legnaro, Italy;
| | - Claudio Cipolat-Gotet
- Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy;
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 1, 35020 Legnaro, Italy;
- Correspondence:
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18
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Bresolin T, Dórea JRR. Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems. Front Genet 2020; 11:923. [PMID: 32973876 PMCID: PMC7468402 DOI: 10.3389/fgene.2020.00923] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/24/2020] [Indexed: 12/17/2022] Open
Abstract
High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.
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Affiliation(s)
- Tiago Bresolin
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - João R R Dórea
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
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19
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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: 27] [Impact Index Per Article: 6.8] [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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20
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Mesgaran SD, Eggert A, Höckels P, Derno M, Kuhla B. The use of milk Fourier transform mid-infrared spectra and milk yield to estimate heat production as a measure of efficiency of dairy cows. J Anim Sci Biotechnol 2020; 11:43. [PMID: 32399210 PMCID: PMC7204237 DOI: 10.1186/s40104-020-00455-0] [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: 12/03/2019] [Accepted: 04/01/2020] [Indexed: 11/10/2022] Open
Abstract
Background Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows. Methods Heat production was computed based on the animal’s consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression. Results Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW0.75. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5 kJ/kg BW0.75. The ratio of performance to deviation (RPD) was 1.56. Conclusion The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.
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Affiliation(s)
- Sadjad Danesh Mesgaran
- 1Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Anja Eggert
- 2Institute of Genetics and Biometry, Leibniz Institute for Farm Anih8mal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Peter Höckels
- IfM GmbH & Co. KG - Institut für Milchuntersuchung (Milk Testing Services North Rhine-Westphalia), Bischofstraße 85, 47809 Krefeld, Germany
| | - Michael Derno
- 1Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Björn Kuhla
- 1Institute of Nutritional Physiology "Oskar Kellner," Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
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21
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Delhez P, Colinet F, Vanderick S, Bertozzi C, Gengler N, Soyeurt H. Predicting milk mid-infrared spectra from first-parity Holstein cows using a test-day mixed model with the perspective of herd management. J Dairy Sci 2020; 103:6258-6270. [PMID: 32418684 DOI: 10.3168/jds.2019-17717] [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: 10/08/2019] [Accepted: 02/27/2020] [Indexed: 11/19/2022]
Abstract
The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems.
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Affiliation(s)
- P Delhez
- National Fund for Scientific Research (FRS-FNRS), Brussels 1000, Belgium; TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium.
| | - F Colinet
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
| | - S Vanderick
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
| | - C Bertozzi
- Walloon Breeding Association (awé Groupe), Ciney 5590, Belgium
| | - N Gengler
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
| | - H Soyeurt
- TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
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22
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Review: Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation. Animal 2020; 14:s2-s16. [PMID: 32024560 DOI: 10.1017/s1751731119003100] [Citation(s) in RCA: 179] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Meat and milk from ruminants provide an important source of protein and other nutrients for human consumption. Although ruminants have a unique advantage of being able to consume forages and graze lands not suitable for arable cropping, 2% to 12% of the gross energy consumed is converted to enteric CH4 during ruminal digestion, which contributes approximately 6% of global anthropogenic greenhouse gas emissions. Thus, ruminant producers need to find cost-effective ways to reduce emissions while meeting consumer demand for food. This paper provides a critical review of the substantial amount of ruminant CH4-related research published in past decades, highlighting hydrogen flow in the rumen, the microbiome associated with methanogenesis, current and future prospects for CH4 mitigation and insights into future challenges for science, governments, farmers and associated industries. Methane emission intensity, measured as emissions per unit of meat and milk, has continuously declined over the past decades due to improvements in production efficiency and animal performance, and this trend is expected to continue. However, continued decline in emission intensity will likely be insufficient to offset the rising emissions from increasing demand for animal protein. Thus, decreases in both emission intensity (g CH4/animal product) and absolute emissions (g CH4/day) are needed if the ruminant industries continue to grow. Providing producers with cost-effective options for decreasing CH4 emissions is therefore imperative, yet few cost-effective approaches are currently available. Future abatement may be achieved through animal genetics, vaccine development, early life programming, diet formulation, use of alternative hydrogen sinks, chemical inhibitors and fermentation modifiers. Individually, these strategies are expected to have moderate effects (<20% decrease), with the exception of the experimental inhibitor 3-nitrooxypropanol for which decreases in CH4 have consistently been greater (20% to 40% decrease). Therefore, it will be necessary to combine strategies to attain the sizable reduction in CH4 needed, but further research is required to determine whether combining anti-methanogenic strategies will have consistent additive effects. It is also not clear whether a decrease in CH4 production leads to consistent improved animal performance, information that will be necessary for adoption by producers. Major constraints for decreasing global enteric CH4 emissions from ruminants are continued expansion of the industry, the cost of mitigation, the difficulty of applying mitigation strategies to grazing ruminants, the inconsistent effects on animal performance and the paucity of information on animal health, reproduction, product quality, cost-benefit, safety and consumer acceptance.
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23
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Pryce JE, Haile-Mariam M. Symposium review: Genomic selection for reducing environmental impact and adapting to climate change. J Dairy Sci 2020; 103:5366-5375. [PMID: 32331869 DOI: 10.3168/jds.2019-17732] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 12/03/2019] [Indexed: 12/18/2022]
Abstract
The world has been warming as greenhouse gases accumulate. Worldwide from 1880 to 2012, the average surface temperature has increased by about 0.85°C and by 0.12°C per decade since 1951. The world's cattle population is a contributor to atmospheric methane, a potent greenhouse gas, in addition to suffering from high temperatures combined with humidity. This makes research into reducing the global footprint of dairy cows of importance on a long-term horizon, while improving tolerance to heat could alleviate the effects of rising temperatures. In December 2017, genomic estimated breeding values for heat tolerance in dairy cattle were released for the first time in Australia. Currently, heat tolerance is not included in the Balanced Performance Index (Australia's national selection index), and the correlation between heat tolerance breeding values and Balanced Performance Index is -0.20, so over time, heat tolerance has worsened due to lack of selection pressure. However, in contrast, sizable reductions in greenhouse gas emissions have been achieved as a favorable response to selecting for increased productivity, longevity, and efficiency, with opportunities for even greater gains through selecting for cow emissions directly. Internationally considerable research effort has been made to develop breeding values focused on reducing methane emissions using individual cow phenotypes. This requires (1) definition of breeding objectives and selection criteria and (2) assembling a sufficiently large data set for genomic prediction. Selecting for heat tolerance and reduced emissions directly may improve resilience to changing environments while reducing environmental impact.
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Affiliation(s)
- Jennie E Pryce
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia.
| | - Mekonnen Haile-Mariam
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
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24
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Tiplady KM, Lopdell TJ, Littlejohn MD, Garrick DJ. The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle. J Anim Sci Biotechnol 2020; 11:39. [PMID: 32322393 PMCID: PMC7164258 DOI: 10.1186/s40104-020-00445-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/09/2020] [Indexed: 11/22/2022] Open
Abstract
Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.
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Affiliation(s)
- K M Tiplady
- 1Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand.,2School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - T J Lopdell
- 1Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand
| | - M D Littlejohn
- 1Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240 New Zealand.,2School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
| | - D J Garrick
- 2School of Agriculture, Massey University, Ruakura, Hamilton, 3240 New Zealand
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25
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Cole JB, Eaglen SAE, Maltecca C, Mulder HA, Pryce JE. The future of phenomics in dairy cattle breeding. Anim Front 2020; 10:37-44. [PMID: 32257602 DOI: 10.1093/af/vfaa007] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Affiliation(s)
- John B Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD
| | | | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC
| | - Han A Mulder
- Department of Animal Sciences, Wageningen University and Research Animal Breeding and Genomics, Wageningen, The Netherlands
| | - Jennie E Pryce
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia
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26
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Delhez P, Ho PN, Gengler N, Soyeurt H, Pryce JE. Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy? J Dairy Sci 2020; 103:3264-3274. [PMID: 32037165 DOI: 10.3168/jds.2019-17473] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 12/10/2019] [Indexed: 01/02/2023]
Abstract
Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion.
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Affiliation(s)
- P Delhez
- National Fund for Scientific Research (F.R.S.-FNRS), Egmont 5, Brussels 1000, Belgium; Terra Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
| | - P N Ho
- Centre for AgriBioscience, AgriBio, Agriculture Victoria, Bundoora, Victoria 3083, Australia.
| | - N Gengler
- Terra Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
| | - H Soyeurt
- Terra Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux 5030, Belgium
| | - J E Pryce
- Centre for AgriBioscience, AgriBio, Agriculture Victoria, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
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27
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Review: Genetic and genomic selection as a methane mitigation strategy in dairy cattle. Animal 2020; 14:s473-s483. [DOI: 10.1017/s1751731120001561] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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28
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Denninger TM, Schwarm A, Dohme-Meier F, Münger A, Bapst B, Wegmann S, Grandl F, Vanlierde A, Sorg D, Ortmann S, Clauss M, Kreuzer M. Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows. J Dairy Sci 2019; 103:2024-2039. [PMID: 31864736 DOI: 10.3168/jds.2019-17101] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 10/22/2019] [Indexed: 11/19/2022]
Abstract
Since heritability of CH4 emissions in ruminants was demonstrated, various attempts to generate large individual animal CH4 data sets have been initiated. Predicting individual CH4 emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH4 emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH4 emitting cows differ in intake, digestion, and efficiency from high CH4 emitters. In the current study, putatively low and putatively high CH4 emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH4 emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH4 emission was measured in 4 open circuit RC. Daily CH4 emissions were also estimated using data from 2 laser CH4 detectors (LMD). The MIR-predicted CH4 production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH4 emissions measured either with RC or estimated using LMD, and no correlation was found between CH4 predictions (MIR) and CH4 emissions measured in RC. When individuals were recategorized based on CH4 yield measured in RC, differences between categories of 10 low and 10 high CH4 emitters were about 20%. Low CH4 emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH4 emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH4 predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH4 emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.
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Affiliation(s)
- T M Denninger
- ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland
| | - A Schwarm
- ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland; Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway
| | - F Dohme-Meier
- Agroscope, Ruminant Research Unit, Route de la Tioleyre 4, 1725 Posieux, Switzerland
| | - A Münger
- Agroscope, Ruminant Research Unit, Route de la Tioleyre 4, 1725 Posieux, Switzerland
| | - B Bapst
- Qualitas AG, Chamerstrasse 56, 6300 Zug, Switzerland
| | - S Wegmann
- Qualitas AG, Chamerstrasse 56, 6300 Zug, Switzerland
| | - F Grandl
- Qualitas AG, Chamerstrasse 56, 6300 Zug, Switzerland
| | - A Vanlierde
- Valorisation of Agricultural Products Department, Walloon Agricultural Research Centre, Chaussée de Namur, 24, B-5030 Gembloux, Belgium
| | - D Sorg
- Institute of Agricultural and Nutritional Sciences - Animal Breeding, Martin Luther University Halle-Wittenberg, Theodor-Lieser-Str. 11, 06120 Halle, Germany; German Environment Agency (Umweltbundesamt), Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany
| | - S Ortmann
- Leibniz Institute for Zoo and Wildlife Research (IZW) Berlin, Alfred-Kowalke-Str. 17, 10315 Berlin, Germany
| | - M Clauss
- Clinic for Zoo Animals, Exotic Pets and Wildlife, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, 8057 Zurich, Switzerland
| | - M Kreuzer
- ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland.
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29
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Denninger TM, Dohme-Meier F, Eggerschwiler L, Vanlierde A, Grandl F, Gredler B, Kreuzer M, Schwarm A, Münger A. Persistence of differences between dairy cows categorized as low or high methane emitters, as estimated from milk mid-infrared spectra and measured by GreenFeed. J Dairy Sci 2019; 102:11751-11765. [PMID: 31587911 DOI: 10.3168/jds.2019-16804] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 08/14/2019] [Indexed: 12/20/2022]
Abstract
Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH4) emitting cows. We investigated the persistence of differences in CH4 emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH4 emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH4 emission was calculated. Traits other than CH4 emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH4 production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH4 data estimated by MIR and measured with GF for individual cows was weak (R2 = 0.26). The categorization based on CH4 production also generated differences in CH4 emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH4 estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH4 production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding.
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Affiliation(s)
- T M Denninger
- Agroscope, Ruminant Research Unit, Route de la Tioleyre 4, 1725 Posieux, Switzerland; ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland
| | - F Dohme-Meier
- Agroscope, Ruminant Research Unit, Route de la Tioleyre 4, 1725 Posieux, Switzerland.
| | - L Eggerschwiler
- Agroscope, Ruminant Research Unit, Route de la Tioleyre 4, 1725 Posieux, Switzerland
| | - A Vanlierde
- Walloon Agricultural Research Centre, Valorisation of Agricultural Products Department, Chaussée de Namur, 24, B-5030 Gembloux, Belgium
| | - F Grandl
- Qualitas AG, Chamerstrasse 56, 6300 Zug, Switzerland; LKV Bayern e.V., Landsberger Str. 282, 80687 München, Germany
| | - B Gredler
- Qualitas AG, Chamerstrasse 56, 6300 Zug, Switzerland
| | - M Kreuzer
- ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland
| | - A Schwarm
- ETH Zurich, Institute of Agricultural Sciences, Universitaetstrasse 2, 8092 Zurich, Switzerland; Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Arboretveien 6, 1433 Ås, Norway
| | - A Münger
- Agroscope, Ruminant Research Unit, Route de la Tioleyre 4, 1725 Posieux, Switzerland
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30
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Review: Metabolic challenges in lactating dairy cows and their assessment via established and novel indicators in milk. Animal 2019; 13:s75-s81. [PMID: 31280745 DOI: 10.1017/s175173111800349x] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The increasing lactational performance of dairy cows over the last few decades is closely related to higher nutritional requirements. The decrease in dry matter intake during the peripartal period results in a considerable mobilisation of body tissues (mainly fat reserves and muscle mass) to compensate for the prevailing lack of energy and nutrients. Despite the activation of adaptive mechanisms to mobilise nutrients from body tissues for maintenance and milk production, the increased metabolic load is still a risk factor for animal health. The prevalence of production diseases, particularly subclinical ketosis is high in the early lactation period. Increased β-hydroxybutyrate (BHB) concentrations further depress gluconeogenesis, feed intake and the immune system. Despite a variety of adaptation responses to nutrient and energy deficit that exists among dairy cows, an early and non-invasive detection of developing metabolic disorders in milk samples would be useful. The frequent and regular milking process of dairy cows creates the ability to obtain samples at any stage of lactation. Routine identification of biomarkers accurately characterising the physiological status of an animal is crucial for decisive strategies. The present overview recapitulates established markers measured in milk that are associated with metabolic health of dairy cows. Specifically, measurements of milk fat, protein, lactose and urea concentrations are evaluated. Changes in the ratio of milk fat to protein may indicate an increased risk for rumen acidosis and ketosis. The costly determination of individual fatty acids in milk creates barriers for grouping of fatty acids into saturated, mono- and polyunsaturated fatty acids. Novel approaches include the potential of mid-IR (MIR) based predictions of BHB and acetone in milk, although the latter are not directly measured, but only estimated via indirect associations of concomitantly altered milk composition during (sub)clinical ketosis. Although MIR-based ketone body concentrations in milk are not suitable to monitor the metabolic status of the individual cow, they provide an estimate of the overall herd or specific groups of animals earlier in a particular stage of lactation. Management decisions can be made earlier and animal health status improved by adjusting diet composition.
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31
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Wang Q, Bovenhuis H. Validation strategy can result in an overoptimistic view of the ability of milk infrared spectra to predict methane emission of dairy cattle. J Dairy Sci 2019; 102:6288-6295. [PMID: 31056328 DOI: 10.3168/jds.2018-15684] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 03/15/2019] [Indexed: 11/19/2022]
Abstract
Because of the environmental impact of methane (CH4), it is of great interest to reduce CH4 emission of dairy cattle and selective breeding might contribute to this. However, this approach requires a rapid and inexpensive measurement technique that can be used to quantify CH4 emission for a large number of individual dairy cows. Milk infrared (IR) spectroscopy has been proposed as a predictor for CH4 emission. In this study, we investigated the feasibility of milk IR spectra to predict breath sensor-measured CH4 of 801 dairy cows on 10 commercial farms. To evaluate the prediction equation, we used random and block cross validation. Using random cross validation, we found a validation coefficient of determination (R2val) of 0.49, which suggests that milk IR spectra are informative in predicting CH4 emission. However, based on block cross validation, with farms as blocks, a negligible R2val of 0.01 was obtained, indicating that milk IR spectra cannot be used to predict CH4 emission. Random cross validation thus results in an overoptimistic view of the ability of milk IR spectra to predict CH4 emission of dairy cows. The difference between the validation strategies could be due to the confounding of farm and date of milk IR analysis, which introduces a correlation between batch effects on the IR analyses and farm-average CH4. Breath sensor-measured CH4 is strongly influenced by farm-specific conditions, which magnifies the problem. Milk IR wavenumbers from water absorption regions, which are generally considered uninformative, showed moderate accuracy (R2val = 0.25) when based on random cross validation, but not when based on block cross validation (R2val = 0.03). These results indicate, therefore, that in the current study, random cross validation results in an overoptimistic view on the ability of milk IR spectra to predict CH4 emission. We suggest prediction based on wavenumbers from water absorption regions as a negative control to identify potential dependence structures in the data.
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Affiliation(s)
- Qiuyu Wang
- Animal Breeding and Genomics Group, Wageningen University, PO Box 338, 6700 AH, Wageningen, the Netherlands
| | - Henk Bovenhuis
- Animal Breeding and Genomics Group, Wageningen University, PO Box 338, 6700 AH, Wageningen, the Netherlands.
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