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Chen Y, Atashi H, Qu J, Delhez P, Runcie D, Soyeurt H, Gengler N. Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding. J Dairy Sci 2024:S0022-0302(24)00975-5. [PMID: 38969006 DOI: 10.3168/jds.2023-24319] [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: 10/17/2023] [Accepted: 06/10/2024] [Indexed: 07/07/2024]
Abstract
With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).
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Affiliation(s)
- Yansen Chen
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium.
| | - Hadi Atashi
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium; Department of Animal Science, Shiraz University, 71441-13131 Shiraz, Iran
| | - Jiayi Qu
- Department of Animal Science, University of California Davis, CA 95616 Davis, USA
| | - Pauline Delhez
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
| | - Daniel Runcie
- Department of Plant Sciences, University of California Davis, CA 95616 Davis, USA
| | - Hélène Soyeurt
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
| | - Nicolas Gengler
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
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Martínez-Marín G, Toledo-Alvarado H, Amalfitano N, Gallo L, Bittante G. Lactation modeling and the effects of rotational crossbreeding on milk production traits and milk-spectra-predicted enteric methane emissions. J Dairy Sci 2024; 107:1485-1499. [PMID: 37944799 DOI: 10.3168/jds.2023-23551] [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: 03/30/2023] [Accepted: 09/19/2023] [Indexed: 11/12/2023]
Abstract
Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production.
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Affiliation(s)
- Gustavo Martínez-Marín
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Nicolò Amalfitano
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Luigi Gallo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
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Uemoto Y, Tomaru T, Masuda M, Uchisawa K, Hashiba K, Nishikawa Y, Suzuki K, Kojima T, Suzuki T, Terada F. Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows. Anim Biosci 2024; 37:173-183. [PMID: 37641824 PMCID: PMC10766487 DOI: 10.5713/ab.23.0120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/27/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
OBJECTIVE This study aimed to evaluate whether the methane (CH4) to carbon dioxide (CO2) ratio (CH4/CO2) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH4 emissions. METHODS The sniffer method was used to simultaneously measure the concentrations of CH4 and CO2 during milking in each milking box of the automatic milking system to obtain CH4/CO2. Methane-related traits, which included CH4 emissions, CH4 per energy-corrected milk, methane conversion factor (MCF), and residual CH4, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH4/CO2 and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets. RESULTS The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH4/CO2 (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH4/CO2 was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH4/CO2 and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms. CONCLUSION This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH4 emissions.
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Affiliation(s)
- Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Sendai 980-8572,
Japan
| | - Tomohisa Tomaru
- Gunma Prefectural Livestock Experiment Station, Maebashi 371-0103,
Japan
| | - Masahiro Masuda
- Niikappu Station, National Livestock Breeding Center (NLBC), Hidaka 056-0141,
Japan
| | - Kota Uchisawa
- Niikappu Station, National Livestock Breeding Center (NLBC), Hidaka 056-0141,
Japan
| | - Kenji Hashiba
- Niikappu Station, National Livestock Breeding Center (NLBC), Hidaka 056-0141,
Japan
| | - Yuki Nishikawa
- Head office, National Livestock Breeding Center (NLBC), Nishigo 961-8061,
Japan
| | - Kohei Suzuki
- Head office, National Livestock Breeding Center (NLBC), Nishigo 961-8061,
Japan
| | - Takatoshi Kojima
- Head office, National Livestock Breeding Center (NLBC), Nishigo 961-8061,
Japan
| | - Tomoyuki Suzuki
- Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Nasushiobara 329-2793,
Japan
| | - Fuminori Terada
- Institute of Livestock and Grassland Science, NARO, Tsukuba 305-0901,
Japan
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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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Dressler EA, Bormann JM, Weaber RL, Rolf MM. Use of methane production data for genetic prediction in beef cattle: A review. Transl Anim Sci 2024; 8:txae014. [PMID: 38371425 PMCID: PMC10872685 DOI: 10.1093/tas/txae014] [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: 09/13/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Methane (CH4) is a greenhouse gas that is produced and emitted from ruminant animals through enteric fermentation. Methane production from cattle has an environmental impact and is an energetic inefficiency. In the beef industry, CH4 production from enteric fermentation impacts all three pillars of sustainability: environmental, social, and economic. A variety of factors influence the quantity of CH4 produced during enteric fermentation, including characteristics of the rumen and feed composition. There are several methodologies available to either quantify or estimate CH4 production from cattle, all with distinct advantages and disadvantages. Methodologies include respiration calorimetry, the sulfur-hexafluoride tracer technique, infrared spectroscopy, prediction models, and the GreenFeed system. Published studies assess the accuracy of the various methodologies and compare estimates from different methods. There are advantages and disadvantages of each technology as they relate to the use of these phenotypes in genetic evaluation systems. Heritability and variance components of CH4 production have been estimated using the different CH4 quantification methods. Agreement in both the amounts of CH4 emitted and heritability estimates of CH4 emissions between various measurement methodologies varies in the literature. Using greenhouse gas traits in selection indices along with relevant output traits could provide producers with a tool to make selection decisions on environmental sustainability while also considering productivity. The objective of this review was to discuss factors that influence CH4 production, methods to quantify CH4 production for genetic evaluation, and genetic parameters of CH4 production in beef cattle.
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Affiliation(s)
- Elizabeth A Dressler
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
| | - Jennifer M Bormann
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
| | - Robert L Weaber
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
| | - Megan M Rolf
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
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Martínez-Marín G, Schiavon S, Tagliapietra F, Cecchinato A, Toledo-Alvarado H, Bittante G. Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level. ITALIAN JOURNAL OF ANIMAL SCIENCE 2023. [DOI: 10.1080/1828051x.2022.2158953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Gustavo Martínez-Marín
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Franco Tagliapietra
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Mexico City, México
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
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Soyeurt H, Wu XL, Grelet C, van Pelt ML, Gengler N, Dehareng F, Bertozzi C, Burchard J. Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models. J Dairy Sci 2023; 106:9095-9104. [PMID: 37678782 DOI: 10.3168/jds.2023-23458] [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: 03/06/2023] [Accepted: 07/07/2023] [Indexed: 09/09/2023]
Abstract
The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored. Moreover, many reference measurements of those phenotypes, needed during the FT-MIR calibration step, are available from past research activities but lack spectra records. So, it is impossible to use them to improve the FT-MIR models. Consequently, there is a strong interest in imputing those missing spectra. The innovative objective of this study was to use the existing large spectral DHI database to estimate missing spectra by selecting probable spectra using, as the match criteria, common dairy traits recorded for a long time by DHI organizations. We tested 4 match criteria combinations. Combination 1 required to have equal fat and protein contents between the sample for which a spectrum was to be estimated and the reference samples in the DHI database. Combination 2 also required an equal urea content. Combination 3 requested equal fat, protein, and lactose contents. Finally, combination 4 included all criteria. When more than one spectrum was found during the search, their average was the estimated spectrum for the query sample. Concretely, this study estimated missing spectra for 1,700 samples using 2,000,000 spectral DHI records. For assessing the effect of this spectral estimation on the prediction quality, FT-MIR equations were used to predict 11 phenotypes, selected as their quantification used different FT-MIR regions. They were related to the milk fat and mineral composition, lactoferrin content, quantity of eructed methane, body weight (BW), and dry matter intake. The accuracy between predictions obtained from actual and estimated spectra was evaluated by calculating the mean absolute error (MAE). The criteria in the fourth and second combinations were too strict to estimate a spectrum for most samples. Indeed, for many samples, no spectra with the same values for those matching criteria was found. The third match criteria combination had a poorer prediction performance for all studied traits and spectral absorptions than the first combination due to fewer matched samples available to compute the missing spectrum. By allowing a range for matching lactose content (±0.1 g/dL milk), we showed that this new combination increased the number of selected samples to compute missing spectra and predict better the infrared absorption at different wavenumbers, especially those related to the lactose quantification. The prediction performance was further improved by performing queries on the entire Walloon DHI spectral database (6,625,570 spectra), and it varied among the studied phenotypes. Without considering the traits used for the matching, the best predictions were obtained for the content of saturated fatty acids (MAE = 0.15 g/dL milk) and BW (MAE = 12.80 kg). Yet, the predictions for the unsaturated fatty acids were less accurate (MAE = 0.13 and 0.018 g/dL milk for monounsaturated and polyunsaturated fatty acids), likely because of the poorer predictions of spectral regions related to long-chain fatty acids. Similarly, poorer predictions were observed for the amount of methane eructed by dairy cows (MAE = 47.02 g/d), likely because it is not directly related to fat content or composition. Prediction accuracies for the remaining traits were also low. In conclusion, we observed that increasing the number of relevant matching criteria helps improve the quality of FT-MIR predicted phenotypes and the number of spectra used during the search. So, it would be of great interest to test in the future the suitability of the developed methodology with large-scale international spectral databases to improve the reliability of EBV from these FT-MIR-based phenotypes and the robustness of FT-MIR predictive models.
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Affiliation(s)
- H Soyeurt
- Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.
| | - X-L Wu
- Council of Dairy Cattle Breeding, Bowie, MD 20716; Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - C Grelet
- Walloon Agricultural Research Center, 5030 Gembloux, Belgium
| | - M L van Pelt
- Cooperation CRV, Animal Evaluation Unit, PO Box 454, 6800 AL Arnhem, the Netherlands
| | - N Gengler
- Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - F Dehareng
- Walloon Agricultural Research Center, 5030 Gembloux, Belgium
| | - C Bertozzi
- Walloon Breeders Association, 5590 Ciney, Belgium
| | - J Burchard
- Council of Dairy Cattle Breeding, Bowie, MD 20716
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Fresco S, Boichard D, Fritz S, Lefebvre R, Barbey S, Gaborit M, Martin P. Comparison of methane production, intensity, and yield throughout lactation in Holstein cows. J Dairy Sci 2023; 106:4147-4157. [PMID: 37105882 DOI: 10.3168/jds.2022-22855] [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: 10/03/2022] [Accepted: 12/28/2022] [Indexed: 04/29/2023]
Abstract
Genetic selection to reduce methane (CH4) emissions from dairy cows is an attractive means of reducing the impact of agricultural production on climate change. In this study, we investigated the feasibility of such an approach by characterizing the interactions between CH4 and several traits of interest in dairy cows. We measured CH4, dry matter intake (DMI), fat- and protein-corrected milk (FPCM), body weight (BW), and body condition score (BCS) from 107 first- and second-parity Holstein cows from December 2019 to November 2021. Methane emissions were measured using a GreenFeed device and expressed in terms of production (MeP, in g/d), yield (MeY, in g/kg DMI), and intensity (MeI, in g/kg FPCM). Because of the limited number of cows, only animal parameters were estimated. Both MeP and MeI were moderately repeatable (>0.45), whereas MeY presented low repeatability, especially in early lactation. Mid lactation was the most stable and representative period of CH4 emissions throughout lactation, with animal correlations above 0.9. The average animal correlations of MeP with DMI, FPCM, and BW were 0.62, 0.48, and 0.36, respectively. The MeI was negatively correlated with FCPM (<-0.5) and DMI (>-0.25), and positively correlated with BW and BCS. The MeY presented stable and weakly positive correlations with the 4 other traits throughout lactation, with the exception of slightly negative animal correlations with FPCM and DMI after the 35th week. The MeP, MeI, and MeY were positively correlated at all lactation stages and, assuming animal and genetic correlations do not strongly differ, selection on one trait should lead to improvements in all. Overall, selection for MeI is probably not optimal as its change would result more from CH4 dilution in increased milk yield than from real decrease in methane emission. Instead, MeY is related to rumen function and is only weakly associated with DMI, FPCM, BW, and BCS; it thus appears to be the most promising CH4 trait for selection, provided that this would not deteriorate feed efficiency and that a system of large-scale phenotyping is developed. The MeP is easier to measure and thus may represent an acceptable alternative, although care would need to be taken to avoid undesirable changes in FPCM and BW.
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Affiliation(s)
- S Fresco
- Eliance, 149 rue de Bercy, 75595 Paris, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France.
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - S Fritz
- Eliance, 149 rue de Bercy, 75595 Paris, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - R Lefebvre
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - S Barbey
- INRAE UE326 Domaine Expérimental du Pin, 61310 Exmes, France
| | - M Gaborit
- INRAE UE326 Domaine Expérimental du Pin, 61310 Exmes, France
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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Kamalanathan S, Houlahan K, Miglior F, Chud TCS, Seymour DJ, Hailemariam D, Plastow G, de Oliveira HR, Baes CF, Schenkel FS. Genetic Analysis of Methane Emission Traits in Holstein Dairy Cattle. Animals (Basel) 2023; 13:ani13081308. [PMID: 37106871 PMCID: PMC10135250 DOI: 10.3390/ani13081308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 04/08/2023] [Indexed: 04/29/2023] Open
Abstract
Genetic selection can be a feasible method to help mitigate enteric methane emissions from dairy cattle, as methane emission-related traits are heritable and genetic gains are persistent and cumulative over time. The objective of this study was to estimate heritability of methane emission phenotypes and the genetic and phenotypic correlations between them in Holstein cattle. We used 1765 individual records of methane emission obtained from 330 Holstein cattle from two Canadian herds. Methane emissions were measured using the GreenFeed system, and three methane traits were analyzed: the amount of daily methane produced (g/d), methane yield (g methane/kg dry matter intake), and methane intensity (g methane/kg milk). Genetic parameters were estimated using univariate and bivariate repeatability animal models. Heritability estimates (±SE) of 0.16 (±0.10), 0.27 (±0.12), and 0.21 (±0.14) were obtained for daily methane production, methane yield, and methane intensity, respectively. A high genetic correlation (rg = 0.94 ± 0.23) between daily methane production and methane intensity indicates that selecting for daily methane production would result in lower methane per unit of milk produced. This study provides preliminary estimates of genetic parameters for methane emission traits, suggesting that there is potential to mitigate methane emission in Holstein cattle through genetic selection.
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Affiliation(s)
- Stephanie Kamalanathan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Kerry Houlahan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Filippo Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
- Lactanet Canada, Guelph, ON N1K 1E5, Canada
| | - Tatiane C S Chud
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dave J Seymour
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
- Centre for Nutrition Modelling, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dagnachew Hailemariam
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Graham Plastow
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Hinayah R de Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
- Lactanet Canada, Guelph, ON N1K 1E5, Canada
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstr. 109a, 3012 Bern, Switzerland
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
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10
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Stocco G, Dadousis C, Pazzola M, Vacca GM, Dettori ML, Mariani E, Cipolat-Gotet C. Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk. Food Chem 2023; 403:134403. [DOI: 10.1016/j.foodchem.2022.134403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/04/2022] [Accepted: 09/22/2022] [Indexed: 10/14/2022]
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11
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Manzanilla-Pech C, Difford G, Løvendahl P, Stephansen R, Lassen J. Genetic (co-)variation of methane emissions, efficiency, and production traits in Danish Holstein cattle along and across lactations. J Dairy Sci 2022; 105:9799-9809. [DOI: 10.3168/jds.2022-22121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/24/2022] [Indexed: 11/17/2022]
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12
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Rumen eukaryotes are the main phenotypic risk factors for larger methane emissions in dairy cattle. Livest Sci 2022. [DOI: 10.1016/j.livsci.2022.105023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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13
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Ghiasi H, Sitkowska B, Piwczyński D, Kolenda M. Genetic Parameters for Methane Emissions Using Indirect Prediction of Methane and Its Association with Milk and Milk Composition Traits. Animals (Basel) 2022; 12:ani12162073. [PMID: 36009662 PMCID: PMC9404742 DOI: 10.3390/ani12162073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/30/2022] Open
Abstract
The study covers milk yield and composition data for 17,468 Polish Holstein-Friesian cows. Methane production (g/lactation per cow, MP) for dairy cow were predicted using three methane production equations (MPE) that took into account: milk yield (MPE1), energy corrected milk (MPE2) and both milk protein concentration (%), and energy-corrected milk (MPE3). The average amounts of methane produced for each cow per lactation were 31,089 g, 46,487 g, and 51,768 g for MPE1, MPE2, and MPE3, respectively. Repeatability models were used to estimate genetic parameters for MP. The estimated heritabilities for MPE1, MPE2, and MPE3 were 0.30, 0.24, and 0.24, respectively, with a standard error of 0.01. High genetic correlations (>0.76) were obtained between methane and milk yield, protein, fat, lactose and dry matter contents in milk for MPE1, MPE2 and MPE3. Still, a moderate genetic correlation (0.34) was obtained between methane and fat content (MPE1); the standard error of the estimated genetic correlation was less than 0.05. The results of the current study indicate that genetic selection aimed to reduce MP in dairy cows is possible. However, such direct genetic selection could cause a negative genetic response in milk yield and composition due to negative genetic correlations between MP and milk yield and composition.
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Affiliation(s)
- Heydar Ghiasi
- Department of Animal Science, Faculty of Agricultural Science, Payame Noor University, Tehran P.O. Box 19395-4697, Iran
| | - Beata Sitkowska
- Department of Animal Biotechnology and Genetics, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85-084 Bydgoszcz, Poland
- Correspondence:
| | - Dariusz Piwczyński
- Department of Animal Biotechnology and Genetics, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85-084 Bydgoszcz, Poland
| | - Magdalena Kolenda
- Department of Animal Biotechnology and Genetics, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85-084 Bydgoszcz, Poland
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Ghavi Hossein-Zadeh N. Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis. Sci Rep 2022; 12:12352. [PMID: 35853993 PMCID: PMC9296463 DOI: 10.1038/s41598-022-16778-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 07/15/2022] [Indexed: 12/26/2022] Open
Abstract
The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (- 0.433 and - 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.
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Affiliation(s)
- Navid Ghavi Hossein-Zadeh
- Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, 41635-1314, Iran.
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15
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Manzanilla-Pech CIV, Stephansen RB, Difford GF, Løvendahl P, Lassen J. Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions. Front Genet 2022; 13:885932. [PMID: 35692829 PMCID: PMC9178123 DOI: 10.3389/fgene.2022.885932] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
In the last decade, several countries have included feed efficiency (as residual feed intake; RFI) in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH4 breath records from 647 Danish Holstein cows measured over 7 years in a research farm were analyzed. Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration (MeC, ppm), methane production (MeP; g/d), methane yield (MeY; g CH4/kg DMI), methane intensity (MeI; g CH4/kg ECM), residual methane concentration (RMeC), residual methane production (RMeP, g/d), and two definitions of residual feed intake with or without including body weight change (RFI1, RFI2). The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used (MeP, RMeP, or MeI). Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.
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Affiliation(s)
| | | | - Gareth Frank Difford
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, As, Norway
| | - Peter Løvendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jan Lassen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- Viking Genetics, Assentoft, Randers, Denmark
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16
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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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17
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Manzanilla-Pech CIV, L Vendahl P, Mansan Gordo D, Difford GF, Pryce JE, Schenkel F, Wegmann S, Miglior F, Chud TC, Moate PJ, Williams SRO, Richardson CM, Stothard P, Lassen J. Breeding for reduced methane emission and feed-efficient Holstein cows: An international response. J Dairy Sci 2021; 104:8983-9001. [PMID: 34001361 DOI: 10.3168/jds.2020-19889] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 04/14/2021] [Indexed: 01/23/2023]
Abstract
Selecting for lower methane (CH4) emitting animals is one of the best approaches to reduce CH4 given that genetic progress is permanent and cumulative over generations. As genetic selection requires a large number of animals with records and few countries actively record CH4, combining data from different countries could help to expedite accurate genetic parameters for CH4 traits and build a future genomic reference population. Additionally, if we want to include CH4 in the breeding goal, it is important to know the genetic correlations of CH4 traits with other economically important traits. Therefore, the aim of this study was first to estimate genetic parameters of 7 suggested methane traits, as well as genetic correlations between methane traits and production, maintenance, and efficiency traits using a multicountry database. The second aim was to estimate genetic correlations within parities and stages of lactation for CH4. The third aim was to evaluate the expected response of economically important traits by including CH4 traits in the breeding goal. A total of 15,320 methane production (MeP, g/d) records from 2,990 cows belonging to 4 countries (Canada, Australia, Switzerland, and Denmark) were analyzed. Records on dry matter intake (DMI), body weight (BW), body condition score, and milk yield (MY) were also available. Additional traits such as methane yield (MeY; g/kg DMI), methane intensity (MeI; g/kg energy-corrected milk), a genetic standardized methane production, and 3 definitions of residual methane production (g/d), residual feed intake, metabolic BW (MBW), BW change, and energy-corrected milk were calculated. The estimated heritability of MeP was 0.21, whereas heritability estimates for MeY and MeI were 0.30 and 0.38, and for the residual methane traits heritability ranged from 0.13 to 0.16. Genetic correlations between different methane traits were moderate to high (0.41 to 0.97). Genetic correlations between MeP and economically important traits ranged from 0.29 (MY) to 0.65 (BW and MBW), being 0.41 for DMI. Selection index calculations showed that residual methane had the most potential for inclusion in the breeding goal when compared with MeP, MeY, and MeI, as residual methane allows for selection of low methane emitting animals without compromising other economically important traits. Inclusion of residual feed intake in the breeding goal could further reduce methane, as the correlation with residual methane is moderate and elicits a favorable correlated response. Adding a negative economic value for methane could facilitate a substantial reduction in methane emissions while maintaining an increase in milk production.
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Affiliation(s)
- C I V Manzanilla-Pech
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark.
| | - P L Vendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - D Mansan Gordo
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - G F Difford
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - J E Pryce
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - F Schenkel
- Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | | | - F Miglior
- Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - T C Chud
- Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - P J Moate
- Centre for Agricultural Innovation, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Victoria 3083, Australia; Agriculture Victoria Research, Ellinbank, Victoria 3820, Australia
| | - S R O Williams
- Agriculture Victoria Research, Ellinbank, Victoria 3820, Australia
| | - C M Richardson
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - P Stothard
- Faculty of Agricultural, Life and Environmental Science, Agriculture, Food and Nutrition Sciences Department, University of Alberta, Edmonton, AB, T6G 2C8, Canada
| | - J Lassen
- Viking Genetics, Ebeltoftvej 16, Assenstoft, 8960 Randers, Denmark
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18
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Uemoto Y, Takeda M, Ogino A, Kurogi K, Ogawa S, Satoh M, Terada F. Genetic and genomic analyses for predicted methane-related traits in Japanese Black steers. Anim Sci J 2020; 91:e13383. [PMID: 32410280 PMCID: PMC7379199 DOI: 10.1111/asj.13383] [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: 01/25/2020] [Revised: 04/01/2020] [Accepted: 04/10/2020] [Indexed: 12/26/2022]
Abstract
The objectives of this study were to estimate genetic parameters and to perform a genome‐wide association study (GWAS) for predicted methane‐related traits in Japanese Black steers. The methane production and yield traits were predicted using on‐farm measurable traits, such as dry matter intake and average daily gain. A total of 4,578 Japanese Black steers, which were progenies of 362 sires genotyped with imputed 551,995 single nucleotide polymorphisms (SNPs), had phenotypes of predicted methane‐related traits during the total fattening period (52 weeks). For the estimation of genetic parameters, the estimated heritabilities were moderate (ranged from 0.57 to 0.60). In addition, the estimated genetic correlations of methane production traits with most of carcass traits and feed‐efficiency traits were unfavorable, but those of methane yield traits were favorable or low. For the GWAS, no genome‐wide significant SNP was detected, but a total of four quantitative trait locus (QTL) regions that explained more than 5.0% of genetic variance were localized on the genome, and some candidate genes associated with growth and feed‐efficiency traits were located on the regions. Our results suggest that the predicted methane‐related traits are heritable and some QTL regions for the traits are localized on the genome in Japanese Black steers.
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Affiliation(s)
- Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | | | - Atushi Ogino
- Maebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc., Maebashi, Japan
| | - Kazuhito Kurogi
- Cattle Breeding Department, Livestock Improvement Association of Japan, Inc., Tokyo, Japan
| | - Shinichro Ogawa
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Masahiro Satoh
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Fuminori Terada
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
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19
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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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20
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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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Reintke J, Brügemann K, Yin T, Engel P, Wagner H, Wehrend A, König S. Assessment of methane emission traits in ewes using a laser methane detector: genetic parameters and impact on lamb weaning performance. Arch Anim Breed 2020; 63:113-123. [PMID: 32363232 PMCID: PMC7191252 DOI: 10.5194/aab-63-113-2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 03/18/2020] [Indexed: 12/24/2022] Open
Abstract
The aim of the present study was to derive individual
methane (CH4) emissions in ewes separated in CH4 respiration and eructation
traits. The generated longitudinal CH4 data structure was used to estimate
phenotypic and genetic relationships between ewe CH4 records and energy
efficiency indicator traits from same ewes as well as from their lambs
(intergenerational perspective). In this regard, we recorded CH4 emissions
via mobile laser methane detector (LMD) technique, body weight (EBW),
backfat thickness (BFT) and body condition score (BCS) from 330 ewes (253
Merinoland (ML), 77 Rhön sheep (RH)) and their 629 lambs (478 ML, 151 RH). The interval between repeated measurements (for ewe traits and lamb
body weight (LBW)) was 3 weeks during lactation. For methane
concentration (µL L-1) determinations in the exhaled air, we
considered short time measurements (3 min). Afterwards, CH4 emissions
were portioned into a respiration and eructation fraction, based on a double
normal distribution. Data preparation enabled the following CH4 trait
definitions: mean CH4 concentration during respiration and eructation
(CH4r+e), mean CH4 concentration during respiration (CH4r), mean CH4
concentration during eructation (CH4e), sum of CH4 concentrations per minute
during respiration (CH4rsum), sum of CH4 concentrations per minute during
eructation (CH4esum), maximal CH4 concentration during respiration
(CH4rmax), maximal CH4 concentration during eructation (CH4emax), and
eructation events per minute (CH4event). Large levels of ewe CH4 emissions
representing energy losses were significantly associated with lower LBW
(P<0.05), lower EBW (P<0.01) and lower BFT (P<0.05). For genetic parameter estimations, we
applied single- and multiple-trait animal models. Heritabilities and additive
genetic variances for CH4 traits were small, i.e., heritabilities in the
range from <0.01 (CH4r+e, CH4r, CH4rmax, CH4esum) to 0.03
(CH4rsum). We estimated negative genetic correlations between CH4 traits and
EBW in the range from -0.44 (CH4r+e) to -0.05 (CH4rsum). Most of the CH4
traits were genetically negatively correlated with BCS (-0.81 for CH4esum)
and with BFT (-0.72 for CH4emax), indicating same genetic mechanisms for CH4
output and energy efficiency indicators. Addressing the intergenerational
aspect, genetic correlations between CH4 emissions from ewes and LBW ranged
between -0.35 (CH4r+e) and 0.01 (CH4rsum, CH4rmax), indicating that
breeding on reduced CH4 emissions (especially eructation traits) contribute
to genetic improvements in lamb weaning performance.
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Affiliation(s)
- Jessica Reintke
- Institute of Animal Breeding and Pet Genetics, University of Giessen, 35390 Giessen, Germany
| | - Kerstin Brügemann
- Institute of Animal Breeding and Pet Genetics, University of Giessen, 35390 Giessen, Germany
| | - Tong Yin
- Institute of Animal Breeding and Pet Genetics, University of Giessen, 35390 Giessen, Germany
| | - Petra Engel
- Institute of Animal Breeding and Pet Genetics, University of Giessen, 35390 Giessen, Germany
| | - Henrik Wagner
- Clinic for Obstetrics, Gynaecology and Andrology of Large and Small Animals with Veterinary Ambulance, University of Giessen, 35392 Giessen, Germany
| | - Axel Wehrend
- Clinic for Obstetrics, Gynaecology and Andrology of Large and Small Animals with Veterinary Ambulance, University of Giessen, 35392 Giessen, Germany
| | - Sven König
- Institute of Animal Breeding and Pet Genetics, University of Giessen, 35390 Giessen, Germany
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Bittante G, Cecchinato A. Heritability estimates of enteric methane emissions predicted from fatty acid profiles, and their relationships with milk composition, cheese-yield and body size and condition. ITALIAN JOURNAL OF ANIMAL SCIENCE 2019. [DOI: 10.1080/1828051x.2019.1698979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- G. Bittante
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente (DAFNAE), University of Padova, Legnaro, Italy
| | - A. Cecchinato
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente (DAFNAE), University of Padova, Legnaro, Italy
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23
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Ornelas LTC, Silva DC, Tomich TR, Campos MM, Machado FS, Ferreira AL, Maurício RM, Pereira LGR. Differences in methane production, yield and intensity and its effects on metabolism of dairy heifers. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 689:1133-1140. [PMID: 31466153 DOI: 10.1016/j.scitotenv.2019.06.489] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/24/2019] [Accepted: 06/28/2019] [Indexed: 06/10/2023]
Abstract
The effects of divergent phenotypic classification in crossbreed Holstein × Gyr dairy heifers for methane emissions in relation to performance, digestibility, energy and nitrogen partition, blood metabolites and temperature of body surface were evaluated. Thirty-five heifers were classified as high and low emission for CH4 production (g/day), yield (g/kg dry matter intake) and intensity (g/kg average daily gain). Digestibility was evaluated by total collection of feces and urine. Gas exchanges were obtained in open-circuit respiratory chambers. A completely randomized design was used and divergent groups were compared by Fisher's test. No differences were found in intake traits between groups of CH4 production and intensity. The low yield group had higher intake. For digestibility and temperature at different body sites were no differences between variables. High production group had higher energy losses as methane and heat production. Low intensity group had higher digestible energy, energy balance and ratio between metabolizable and digestible energy. Urinary nitrogen was 14.3% lower for low production group. There was a difference between methane yield divergent groups for nitrogen intake, digestible and retained. Energy and nitrogen partitioning traits are correlated to the animals divergent for methane production and yield. The low production group presented lower blood insulin concentration. It was not possible to identify divergent animals for CH4 emission using the infrared thermography technique.
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Affiliation(s)
- L T C Ornelas
- Department of Animal Science, State University of Southwestern Bahia (UESB), Bahia 45700-000, Brazil
| | - D C Silva
- Department of Animal Science, State University of Southwestern Bahia (UESB), Bahia 45700-000, Brazil
| | - T R Tomich
- Embrapa Dairy Cattle, Minas Gerais 36038-330, Brazil
| | - M M Campos
- Embrapa Dairy Cattle, Minas Gerais 36038-330, Brazil
| | - F S Machado
- Embrapa Dairy Cattle, Minas Gerais 36038-330, Brazil
| | - A L Ferreira
- Department of Agricultural Science, Federal University of São João del-Rei (UFSJ), Minas Gerais 36307-352, Brazil
| | - R M Maurício
- Department of Agricultural Science, Federal University of São João del-Rei (UFSJ), Minas Gerais 36307-352, Brazil
| | - L G R Pereira
- Embrapa Dairy Cattle, Minas Gerais 36038-330, Brazil.
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24
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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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Genomic-polygenic and polygenic predictions for milk yield, fat yield, and age at first calving in Thai multibreed dairy population using genic and functional sets of genotypes. Livest Sci 2019. [DOI: 10.1016/j.livsci.2018.11.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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26
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Bittante G, Cipolat-Gotet C. Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier-transform infrared spectra. J Dairy Sci 2018; 101:7219-7235. [DOI: 10.3168/jds.2017-14289] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 04/17/2018] [Indexed: 11/19/2022]
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Relationships between milk mid-IR predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds. Animal 2017; 12:1981-1989. [PMID: 29271329 DOI: 10.1017/s1751731117003378] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P<0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performance (e.g. longer calving interval (r=-0.21) and higher culling rate (r=-0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices.
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