1
|
Worku D. Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions. Anim Biotechnol 2024; 35:2362677. [PMID: 38860914 DOI: 10.1080/10495398.2024.2362677] [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] [Indexed: 06/12/2024]
Abstract
Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.
Collapse
Affiliation(s)
- Destaw Worku
- Department of Animal Science, College of Agriculture, Food and Climate Science, Injibara University, Injibara, Ethiopia
| |
Collapse
|
2
|
Kjeldsen MH, de Evan Rozada T, Noel SJ, Schönherz A, Hellwing ALF, Lund P, Weisbjerg MR. Phenotypic traits related to methane emissions from Holstein dairy cows challenged by low or high forage proportion. J Dairy Sci 2024:S0022-0302(24)01111-1. [PMID: 39245171 DOI: 10.3168/jds.2024-24848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 08/05/2024] [Indexed: 09/10/2024]
Abstract
Limited literature is available identifying phenotypical traits related to enteric methane (CH4) production from dairy cows, despite its relevance in relation to breeding for animals with a low CH4 yield (g/kg DMI), and the derived consequences hereof. This study aimed to investigate the relationships between CH4 yield and different animal phenotypes when 16 2nd parity dairy cows, fitted with a ruminal cannula, were fed 2 diets differing in forage:concentrate ratio in a crossover design. The diets had either a low forage proportion (35% on DM basis, F35) or a high forage proportion (63% on DM basis, F63). Gas exchange was measured by means of indirect calorimetry. Spot samples of feces were collected, and indigestible NDF (INDF) was used as an internal marker to determine total-tract digestibility. In addition, ruminal evacuations, monitoring of chewing activity, determination of ruminal VFA concentration, analysis of relative abundance of methanogens, and measurement of liquid passage rate were performed. Statistical differences were analyzed by a linear mixed model with diet, days in milk, and period as fixed effects, and cow as random effect. The random cow estimates (RCE) were extracted from the model to get the Pearson correlations (r) between RCE of CH4 yield with RCE of all other variables measured, to identify possible phenotypes related to CH4 yield. Significant correlations were observed between RCE of CH4 yield and RCE of OM digestibility (r = 0.63) and ruminal concentration of valeric acid (r = -0.61), acetic acid (r = 0.54), ammonium (r = 0.55), and lactic acid (r = ‒0.53). Additionally, tendencies were observed for correlations between RCE of CH4 yield and RCE of H2 yield in g/kg DM (r = 0.47, P = 0.07), and ruminal isobutyric acid concentration (r = 0.43, P = 0.09). No correlations were observed between RCE of CH4 yield and RCE of ruminal pool sizes, milk data, urinary measurements, or chewing activity. Cows had a lower DMI and ECM, when they were fed F63 compared with F35. Cows fed F63 had higher NDF digestibility, CH4 emissions (g/d, g/kg of DMI, and g/kg of ECM), ruminal concentration of acetic acid, ruminal pH, degradation rate of digestible NDF (DNDF, %/h), and longer rumen retention time (h). Also, rumination and total chewing time (min/kg DMI) were higher for cows fed F63. The results in the present study emphasize the positive relation between cow's ability to digest OM and their CH4 emissions. The derived consequences of breeding for lower CH4 emission might be cows with lower ability to digest OM, but more studies are warranted for further documentation of this relationship.
Collapse
Affiliation(s)
- Maria H Kjeldsen
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark.
| | - Trinidad de Evan Rozada
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark
| | - Samantha J Noel
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark
| | - Anna Schönherz
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark
| | - Anne Louise F Hellwing
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark
| | - Peter Lund
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark
| | - Martin R Weisbjerg
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 DK-Tjele, Denmark
| |
Collapse
|
3
|
van Breukelen AE, Veerkamp RF, de Haas Y, Aldridge MN. Genetic parameter estimates for methane emission from breath during lactation and potential inaccuracies in reliabilities assuming a repeatability versus random regression model. J Dairy Sci 2024; 107:5853-5868. [PMID: 38490557 DOI: 10.3168/jds.2024-24285] [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: 10/06/2023] [Accepted: 02/13/2024] [Indexed: 03/17/2024]
Abstract
Methane emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and establishing optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm) recorded in the feed bins of milking robots on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with first- to fifth-order Legendre polynomials for the additive genetic and within-lactation permanent environmental effect. The mean heritability (± SE) was 0.17 ± 0.04, and the mean within-lactation repeatability was 0.56 ± 0.03. The genetic correlations between DIM were high and ranged from 0.34 ± 0.36 to 1.00 ± <0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± <0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus the RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long of a recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and in lower genetic gains than predicted from the reliability.
Collapse
Affiliation(s)
- A E van Breukelen
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands.
| | - R F Veerkamp
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands
| | - Y de Haas
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands
| | - M N Aldridge
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands
| |
Collapse
|
4
|
Giagnoni G, Friggens NC, Johansen M, Maigaard M, Wang W, Lund P, Weisbjerg MR. How much can performance measures explain of the between-cow variation in enteric methane? J Dairy Sci 2024; 107:4658-4669. [PMID: 38310957 DOI: 10.3168/jds.2023-24094] [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: 08/16/2023] [Accepted: 12/29/2023] [Indexed: 02/06/2024]
Abstract
Enteric CH4 produced from dairy cows contributes to the emission of greenhouse gases from anthropogenic sources. Recent studies have shown that the selection of lower CH4-emitting cows is possible, but doing so would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emissions from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A dataset with 3 experiments and a total of 149 lactating dairy cows with repeated measures was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed (C-Lock, Rapid City, SD). The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53 when only performance measures were used, and to 0.66 when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure because the latter simplifies the model and avoids over-fitting. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.
Collapse
Affiliation(s)
- Giulio Giagnoni
- Department of Animal and Veterinary Sciences, AU Viborg Research Centre Foulum, Aarhus University, DK 8830 Tjele, Denmark.
| | - Nicolas C Friggens
- Université Paris Saclay, INRAE, AgroParisTech, UMR 0791 MoSAR, 91120 Palaiseau, France
| | - Marianne Johansen
- Department of Animal and Veterinary Sciences, AU Viborg Research Centre Foulum, Aarhus University, DK 8830 Tjele, Denmark
| | - Morten Maigaard
- Department of Animal and Veterinary Sciences, AU Viborg Research Centre Foulum, Aarhus University, DK 8830 Tjele, Denmark
| | - Wenji Wang
- Department of Animal and Veterinary Sciences, AU Viborg Research Centre Foulum, Aarhus University, DK 8830 Tjele, Denmark
| | - Peter Lund
- Department of Animal and Veterinary Sciences, AU Viborg Research Centre Foulum, Aarhus University, DK 8830 Tjele, Denmark
| | - Martin R Weisbjerg
- Department of Animal and Veterinary Sciences, AU Viborg Research Centre Foulum, Aarhus University, DK 8830 Tjele, Denmark.
| |
Collapse
|
5
|
Bošnjaković D, Nedić S, Arsić S, Prodanović R, Vujanac I, Jovanović L, Stojković M, Jovanović IB, Djuricic I, Kirovski D. Effects of Brown Seaweed ( Ascophyllum nodosum) Supplementation on Enteric Methane Emissions, Metabolic Status and Milk Composition in Peak-Lactating Holstein Cows. Animals (Basel) 2024; 14:1520. [PMID: 38891568 PMCID: PMC11171174 DOI: 10.3390/ani14111520] [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: 04/05/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/21/2024] Open
Abstract
The dairy industry contributes significantly to anthropogenic methane emissions, which have an impact on global warming. This study aimed to investigate the effects of a dietary inclusion of brown seaweed Ascophyllum nodosum on enteric methane emissions (EMEs), hematological and blood biochemical profiles, and milk composition in dairy cows. Eighteen Holstein cows were divided into three groups: CON (non-supplemented cows), BS50 (50 mL of 10% A. nodosum), and BS100 (100 mL of 10% A. nodosum). In each cow, measurements of EME, dry matter intake (DMI), and milk yield (MY), as well as blood and milk sampling with respective analyzes, were performed before supplementation (P1), after 15 (P2) days, and after 30 (P3) days of supplementation. A. nodosum reduced (p < 0.05) methane production, methane yield, and methane intensity in both BS50 and BS100, and raised DMI (p < 0.05) only in BS50. Total bilirubin (p < 0.05) was higher in BS50 compared to CON cows in P2, and triacylglycerols were lower (p < 0.05) in BS50 than in CON cows in P3. Higher milk fat content was found in BS50 than in CON cows in P3. C16:0 proportions were higher (p < 0.05) in BS50 and BS100 than in CON cows, while C18:3n-3 was higher (p < 0.05) in BS100 than in BS50 and CON cows in P3. Dietary treatment with A. nodosum reduced EMEs and showed the potential to increase DMI and to improve energy status as well as milk composition in peak-lactating dairy cows.
Collapse
Affiliation(s)
- Dušan Bošnjaković
- Department of Physiology and Biochemistry, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (D.B.); (L.J.); (M.S.); (I.B.J.)
| | - Sreten Nedić
- Department of Ruminant and Swine Diseases, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (S.N.); (S.A.); (R.P.); (I.V.)
| | - Sveta Arsić
- Department of Ruminant and Swine Diseases, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (S.N.); (S.A.); (R.P.); (I.V.)
| | - Radiša Prodanović
- Department of Ruminant and Swine Diseases, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (S.N.); (S.A.); (R.P.); (I.V.)
| | - Ivan Vujanac
- Department of Ruminant and Swine Diseases, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (S.N.); (S.A.); (R.P.); (I.V.)
| | - Ljubomir Jovanović
- Department of Physiology and Biochemistry, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (D.B.); (L.J.); (M.S.); (I.B.J.)
| | - Milica Stojković
- Department of Physiology and Biochemistry, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (D.B.); (L.J.); (M.S.); (I.B.J.)
| | - Ivan B. Jovanović
- Department of Physiology and Biochemistry, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (D.B.); (L.J.); (M.S.); (I.B.J.)
| | - Ivana Djuricic
- Department of Bromatology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11221 Belgrade, Serbia;
| | - Danijela Kirovski
- Department of Physiology and Biochemistry, Faculty of Veterinary Medicine, University of Belgrade, Bulevar Oslobodjenja 18, 11000 Belgrade, Serbia; (D.B.); (L.J.); (M.S.); (I.B.J.)
| |
Collapse
|
6
|
Fresco S, Boichard D, Lefebvre R, Barbey S, Gaborit M, Fritz S, Martin P. Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows. Animal 2024; 18:101110. [PMID: 38442541 DOI: 10.1016/j.animal.2024.101110] [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: 10/05/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/07/2024] Open
Abstract
The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from -0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.
Collapse
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
| | - 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
| | - S Fritz
- Eliance, 149 rue de Bercy, 75595 Paris, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Souza LL, Dominguez-Castaño P, Gianvecchio SB, Sakamoto LS, Rodrigues GRD, Soares TLDS, Bonilha SFM, Marcatto JDOS, Galvão Albuquerque L, Vasconcelos Silva JAII, Zerlotti Mercadante ME. Heritability estimates and genome-wide association study of methane emission traits in Nellore cattle. J Anim Sci 2024; 102:skae182. [PMID: 38967061 PMCID: PMC11282363 DOI: 10.1093/jas/skae182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 07/03/2024] [Indexed: 07/06/2024] Open
Abstract
The objectives of the present study were to estimate the heritability for daily methane emission (CH4) and residual daily methane emission (CH4res) in Nellore cattle, as well as to perform genome-wide association studies (GWAS) to identify genomic regions and candidate genes influencing the genetic variation of CH4 and CH4res. Methane emission phenotypes of 743 Nellore animals belonging to 3 breeding programs were evaluated. CH4 was measured using the sulfur hexafluoride (SF6) tracer technique (which involves an SF6 permeation tube introduced into the rumen, and an appropriate apparatus on each animal), and CH4res was obtained as the difference between observed CH4 and CH4 adjusted for dry matter intake. A total of 6,252 genotyped individuals were used for genomic analyses. Data were analyzed with a univariate animal model by the single-step GBLUP method using the average information restricted maximum likelihood (AIREML) algorithm. The effects of single nucleotide polymorphisms (SNPs) were obtained using a single-step GWAS approach. Candidate genes were identified based on genomic windows associated with quantitative trait loci (QTLs) related to the 2 traits. Annotation of QTLs and identification of candidate genes were based on the initial and final coordinates of each genomic window considering the bovine genome ARS-UCD1.2 assembly. Heritability estimates were of moderate to high magnitude, being 0.42 ± 0.09 for CH4 and 0.21 ± 0.09 for CH4res, indicating that these traits will respond rapidly to genetic selection. GWAS revealed 11 and 15 SNPs that were significantly associated (P < 10-6) with genetic variation of CH4 and CH4res, respectively. QTLs associated with feed efficiency, residual feed intake, body weight, and height overlapped with significant markers for the traits evaluated. Ten candidate genes were present in the regions of significant SNPs; 3 were associated with CH4 and 7 with CH4res. The identified genes are related to different functions such as modulation of the rumen microbiota, fatty acid production, and lipid metabolism. CH4 and CH4res presented sufficient genetic variation and may respond rapidly to selection. Therefore, these traits can be included in animal breeding programs aimed at reducing enteric methane emissions across generations.
Collapse
Affiliation(s)
- Luana Lelis Souza
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
| | - Pablo Dominguez-Castaño
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
- Facultad de Ciencias Agrarias, Fundación Universitaria Agraria de Colombia-UNIAGRARIA, Bogotá 111166, Colombia
| | - Sarah Bernardes Gianvecchio
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
- Institute of Animal Science (IZ), Beef Cattle Research Center, 14160-970, Sertãozinho, Brazil
| | | | - Gustavo Roberto Dias Rodrigues
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
- Institute of Animal Science (IZ), Beef Cattle Research Center, 14160-970, Sertãozinho, Brazil
| | - Tainara Luana da Silva Soares
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
- Institute of Animal Science (IZ), Beef Cattle Research Center, 14160-970, Sertãozinho, Brazil
| | - Sarah Figueiredo Martins Bonilha
- Institute of Animal Science (IZ), Beef Cattle Research Center, 14160-970, Sertãozinho, Brazil
- National Council for Science and Technological Development, 71605-001, Brasilia, Brazil
| | | | - Lucia Galvão Albuquerque
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
- National Council for Science and Technological Development, 71605-001, Brasilia, Brazil
| | - Josineudson Augusto II Vasconcelos Silva
- Faculty of Veterinary Medicine and Animal Science, São Paulo State University (UNESP), 18618-000, Botucatu, Brazil
- National Council for Science and Technological Development, 71605-001, Brasilia, Brazil
| | - Maria Eugênia Zerlotti Mercadante
- Faculty of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), 14884-900, Jaboticabal, Brazil
- Institute of Animal Science (IZ), Beef Cattle Research Center, 14160-970, Sertãozinho, Brazil
- National Council for Science and Technological Development, 71605-001, Brasilia, Brazil
| |
Collapse
|
10
|
Kim M, Masaki T, Oikawa K, Ashihara A, Ikuta K, Iwamoto E, Lee H, Haga S, Uemoto Y, Roh S, Terada F, Nonaka I. Effect of residual methane emission on physiological characteristics and carcass performance in Japanese Black cattle. Anim Sci J 2024; 95:e13954. [PMID: 38797605 DOI: 10.1111/asj.13954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/23/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024]
Abstract
This study investigated the physiological characteristics and carcass performance associated with residual methane emissions (RME), and the effects of bull differences on CH4-related traits in Japanese Black cattle. Enteric methane (CH4) emissions from 156 Japanese Black cattle (111 heifers and 45 steers) were measured during early fattening using the sniffer method. Various physiological parameters were investigated to clarify the physiological traits between the high, middle, and low RME groups. CH4-related traits were examined to determine whether bull differences affected progeny CH4 emissions. Ruminal butyrate and NH3 concentrations were significantly higher in the high-RME group than in the low-RME group, whereas the propionate content was significantly higher in the low-RME group. Blood urea nitrogen, β-hydroxybutyric acid, and insulin concentrations were significantly higher, and blood amino acids were lower in the high-RME group than in the other groups. No significant differences were observed in the carcass traits and beef fat composition between RME groups. CH4-related traits were significantly different among bull herds. Our results show that CH4-related traits are heritable, wherein bull differences affect progeny CH4 production capability, and that the above-mentioned rumen fermentations and blood metabolites could be used to evaluate enteric methanogenesis in Japanese Black cattle.
Collapse
Affiliation(s)
- Minji Kim
- Institute of Livestock and Grassland Science, Tsukuba, Ibaraki, Japan
| | - Tatsunori Masaki
- Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries, Kasai, Hyogo, Japan
| | - Kohei Oikawa
- Institute of Livestock and Grassland Science, Nasushiobara, Tochigi, Japan
| | - Akane Ashihara
- Institute of Livestock and Grassland Science, Tsukuba, Ibaraki, Japan
| | - Kentaro Ikuta
- Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries, Kasai, Hyogo, Japan
| | - Eiji Iwamoto
- Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries, Kasai, Hyogo, Japan
| | - Huseong Lee
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Satoshi Haga
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Sanggun Roh
- Graduate School of Agricultural Science, Tohoku University, Sendai, Japan
| | - Fuminori Terada
- Institute of Livestock and Grassland Science, Tsukuba, Ibaraki, Japan
| | - Itoko Nonaka
- Institute of Livestock and Grassland Science, Tsukuba, Ibaraki, Japan
| |
Collapse
|
11
|
Madilindi MA, Zishiri OT, Dube B, Banga CB. Genetic parameter estimates for daily predicted gross feed efficiency and its association with energy-corrected milk in South African Holstein cattle. Trop Anim Health Prod 2023; 55:339. [PMID: 37770720 PMCID: PMC10539442 DOI: 10.1007/s11250-023-03741-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/12/2023] [Indexed: 09/30/2023]
Abstract
Genetic parameters for daily predicted gross feed efficiency (pGFE) and energy corrected milk (ECM) in the first three parities of South African Holstein cattle were estimated by repeatability animal models. Data comprised of 11,068 test-day milk production records of 1,575 Holstein cows that calved between 2009 and 2019. Heritability estimates for pGFE were 0.12 ± 0.06, 0.09 ± 0.04 and 0.18 ± 0.05 in early, mid and late lactation, respectively. Estimates were moderate for primiparous (0.21 ± 0.05) and low for multiparous (0.10 ± 0.04) cows. Heritability and repeatability across all lactations were 0.14 ± 0.03 and 0.37 ± 0.03, respectively. Genetic correlations between pGFE in different stages of lactation ranged from 0.87 ± 0.24 (early and mid) to 0.97 ± 0.28 (early and late), while a strong genetic correlation (0.90 ± 0.03) was found between pGFE and ECM, across all lactations. The low to moderate heritability estimates for pGFE suggest potential for genetic improvement of the trait through selection, albeit with a modest accuracy of selection. The high genetic correlation of pGFE with ECM may, however, assist to improve accuracy of selection for feed efficiency by including both traits in multi-trait analyses. These genetic parameters may be used to estimate breeding values for pGFE, which will enable the trait to be incorporated in the breeding objective for South African Holstein cattle.
Collapse
Affiliation(s)
- Matome A Madilindi
- Discipline of Genetics, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Private Bag X54001, Durban, 4000, South Africa.
- ARC-Animal Production, Private Bag X2, Irene, 0062, South Africa.
| | - Oliver T Zishiri
- Discipline of Genetics, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Private Bag X54001, Durban, 4000, South Africa
| | - Bekezela Dube
- ARC-Animal Production, Private Bag X2, Irene, 0062, South Africa
| | - Cuthbert B Banga
- Department of Animal Sciences, Faculty of Science, Tshwane University of Technology, Private Bag X680, Pretoria, 0001, South Africa
- Department of Agriculture and Animal Health, University of South Africa, Private Bag X6, Florida, 1710, South Africa
- Department of Animal Sciences, Faculty of Animal and Veterinary Sciences, Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana
| |
Collapse
|
12
|
Stepanchenko N, Stefenoni H, Hennessy M, Nagaraju I, Wasson DE, Cueva SF, Räisänen SE, Dechow CD, Pitta DW, Hristov AN. Microbial composition, rumen fermentation parameters, enteric methane emissions, and lactational performance of phenotypically high and low methane-emitting dairy cows. J Dairy Sci 2023; 106:6146-6170. [PMID: 37479584 DOI: 10.3168/jds.2022-23190] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/05/2023] [Indexed: 07/23/2023]
Abstract
This experiment was designed to investigate the relation of high and low methane-yield phenotypes with body weight (BW), dry matter intake (DMI), lactation performance, enteric CH4 emissions, and rumen fermentation parameters in lactating dairy cows. A total of 130 multi- and primiparous Holstein cows were screened for enteric CH4 emissions using the GreenFeed system (C-Lock Inc.). Out of these 130 cows, 5 were identified as phenotypically high (HM) and 5 as phenotypically low (LM) CH4 emitters. Cows in the LM group had lower daily enteric CH4 emissions than cows in the HM group (on average 346 vs. 439 g/d, respectively), lower CH4 yield (15.5 vs. 20.4 g of CH4/kg of DMI), and CH4 intensity (13.2 vs. 17.0 g of CH4/ kg of energy-corrected milk yield). Enteric emissions of CO2 and H2 did not differ between HM and LM cows. These 10 cows were blocked by parity, days in milk, and milk production, and were used in a 5-wk randomized complete block design experiment. Milk composition, production, and BW were also not different between LM and HM cows. The concentration of total volatile fatty acids in ruminal contents did not differ between CH4 phenotypes, but LM cows had a lower molar proportion of acetate (57 vs. 62.1%), a higher proportion of propionate (27.5 vs. 21.6%, respectively), and therefore a lower acetate-to-propionate ratio than HM cows. Consistently, the 16S cDNA analysis revealed the abundance of Succinivibrionaceae and unclassified Veillonellaceae to be higher in LM cows compared with HM cows, bacteria that were positively correlated with ruminal propionate concentration. Notably, Succinivibrionaceae trigger the formation of propionate via oxaloacetate pathway from phosphoenolpyruvate via Enzyme Commission: 4.1.1.49, which showed a trend to be higher in LM cows compared with HM cows. Additionally, LM cows possessed fewer transcripts of a gene encoding for methyl-CoM reductase enzyme compared with HM. In this study, low and high CH4-yield cows have similar production performance and milk composition, but total-tract apparent digestibility of organic matter and fiber fractions was lower in the former group of animals.
Collapse
Affiliation(s)
- N Stepanchenko
- Department of Animal Science, The Pennsylvania State University, University Park 16802
| | - H Stefenoni
- Department of Animal Science, The Pennsylvania State University, University Park 16802
| | - M Hennessy
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center, Kennett Square 193482
| | - I Nagaraju
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center, Kennett Square 193482
| | - D E Wasson
- Department of Animal Science, The Pennsylvania State University, University Park 16802
| | - S F Cueva
- Department of Animal Science, The Pennsylvania State University, University Park 16802
| | - S E Räisänen
- Department of Animal Science, The Pennsylvania State University, University Park 16802; Department of Agricultural Sciences, University of Helsinki, P.O. Box 28, FI-00014 University of Helsinki, Finland
| | - C D Dechow
- Department of Animal Science, The Pennsylvania State University, University Park 16802
| | - D W Pitta
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center, Kennett Square 193482.
| | - A N Hristov
- Department of Animal Science, The Pennsylvania State University, University Park 16802.
| |
Collapse
|
13
|
Marino R, Petrera F, Abeni F. Scientific Productions on Precision Livestock Farming: An Overview of the Evolution and Current State of Research Based on a Bibliometric Analysis. Animals (Basel) 2023; 13:2280. [PMID: 37508057 PMCID: PMC10376211 DOI: 10.3390/ani13142280] [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: 05/16/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The interest in precision livestock farming (PLF)-a concept discussed for the first time in the early 2000s-has advanced considerably in recent years due to its important role in the development of sustainable livestock production systems. However, a comprehensive bibliometric analysis of the PLF literature is lacking. To address this gap, this study analyzed documents published from 2005 to 2021, aiming to understand the historical influences on technology adoption in livestock farming, identify future global trends, and examine shifts in scientific research on this topic. By using specific search terms in the Web of Science Core Collection, 886 publications were identified and analyzed using the bibliometrix R-package. The analysis revealed that the collection consisted mostly of research articles (74.6%) and reviews (10.4%). The top three core journals were the Journal of Dairy Science, Computers and Electronics in Agriculture, and Animals. Over time, the number of publications has steadily increased, with a higher growth rate in the last five years (29.0%) compared to the initial period (13.7%). Authors and institutions from multiple countries have contributed to the literature, with the USA, the Netherlands, and Italy leading in terms of publication numbers. The analysis also highlighted the growing interest in bovine production systems, emphasizing the importance of behavioral studies in PLF tool development. Automated milking systems were identified as central drivers of innovation in the PLF sector. Emerging themes for the future included "emissions" and "mitigation", indicating a focus on environmental concerns.
Collapse
Affiliation(s)
- Rosanna Marino
- Centro di Ricerca Zootecnia e Acquacoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA), Via Lombardo 11, 26900 Lodi, Italy
| | - Francesca Petrera
- Centro di Ricerca Zootecnia e Acquacoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA), Via Lombardo 11, 26900 Lodi, Italy
| | - Fabio Abeni
- Centro di Ricerca Zootecnia e Acquacoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA), Via Lombardo 11, 26900 Lodi, Italy
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
van Breukelen AE, Aldridge MN, Veerkamp RF, Koning L, Sebek LB, de Haas Y. Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows. J Dairy Sci 2023; 106:4121-4132. [PMID: 37080783 DOI: 10.3168/jds.2022-22735] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/05/2023] [Indexed: 04/22/2023]
Abstract
To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.
Collapse
Affiliation(s)
- A E van Breukelen
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands.
| | - M N Aldridge
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - R F Veerkamp
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - L Koning
- Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - L B Sebek
- Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - Y de Haas
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| |
Collapse
|
16
|
Wang J, Yang H, Chen S, Li W, Yu J, Hu Z, Zhuo Y, Huang Q, Liu Z, Zhou L, Wu J, Wang Z, Guo F, Yun P, Wang X, Liu JF. Genome-wide association study reveals candidate genes for pollution excreta traits in pigs. Anim Genet 2023. [PMID: 37040927 DOI: 10.1111/age.13323] [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: 06/16/2022] [Revised: 11/13/2022] [Accepted: 03/18/2023] [Indexed: 04/13/2023]
Abstract
Excreta traits comprise a very important characteristic in breeding that have been neglected for a long time. With the growth of intensive pig farming, plenty of environment problems have been raised, and people have begun to pay attention to pig excreta behaviors from genetics and breeding perspectives. However, the genetic architecture of excreta traits remains unclear. To investigate the genetic architecture of excreta traits in pigs, eight excreta traits and feed conversion ratio (FCR) were analyzed in this study. We performed genome-wide association studies (GWASs) on 213 Yorkshire pigs and estimated genetic parameters for a total number of 290 pigs, comprising 213 Yorkshire, 52 Landrace and 25 Duroc. After analysis, eight and 22 genome-wide significant SNPs were detected for FCR and the eight excreta traits in single-trait GWASs separately, and 18 were detected in a multi-trait meta-analysis for excreta traits, six of which were detected in both the single-trait and the multi-trait GWAS. Eighty, 182 and 133 genes were detected within 1 Mb of the genome-wide significant SNPs for FCR, excreta traits and multi-trait meta-analysis, respectively. Five candidate genes (BCKDC, DBT, ANKRD7, SHPRH and HCRT) with biochemical and physiological effects relevant to feed efficiency and excreta traits might be interesting markers for future breeding. Meanwhile, functional enrichment analysis indicates that most of the significant pathways are associated with the glutathione catabolic process, DNA topological change and replication fork protection complex. This study reveals the architecture of excreta traits in commercial pigs and offers an opportunity for decreasing the pollution from excreta using genomic selection in pigs.
Collapse
Affiliation(s)
- Junjian Wang
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Huawei Yang
- Shenzhen Kingsino Technology Co., Ltd., 518107, Shenzhen, No.18 Guangdian North Rd, High-Tech Industrial Park, Guangming District, China
| | - Shaokang Chen
- Beijing General Station of Animal Husbandry, 100107, Beijing, China
| | - Weining Li
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Jian Yu
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Zhengzheng Hu
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yue Zhuo
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Qianqian Huang
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Zhen Liu
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lei Zhou
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Jianliang Wu
- Beijing Zhongyu Pig Breeding Co. Ltd., 100194, Beijing, China
| | - Zhaojun Wang
- Beijing Zhongyu Pig Breeding Co. Ltd., 100194, Beijing, China
| | - Feng Guo
- Beijing General Station of Animal Husbandry, 100107, Beijing, China
| | - Peng Yun
- Beijing General Station of Animal Husbandry, 100107, Beijing, China
| | - Xiaofeng Wang
- Beijing General Station of Animal Husbandry, 100107, Beijing, China
| | - Jian-Feng Liu
- State Key Laboratory of animal Biotech Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Methane Emission and Metabolic Status in Peak Lactating Dairy Cows and Their Assessment Via Methane Concentration Profile. ACTA VET-BEOGRAD 2023. [DOI: 10.2478/acve-2023-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
Abstract
Ruminant husbandry contributes to global methane (CH4) emissions and beside its negative impact on the environment, enteric CH4 emissions cause a loss of gross energy intake in cows. The study is aimed to estimate CH4 emission and metabolic status in dairy cows via the methane concentration profile as a tool for analyzing the CH4 production pattern. The study included eighteen cows whose enteric CH4 emission was measured during three consecutive days in three periods: 2 hours before (P1), 2–4 hours (P2) and 6–8 hours (P3) after the morning feeding. Based on CH4 enteric emissions, cows were divided into two groups (n=6, respectively): HM (average CH4 concentration: 5430.08 ± 365.92 ppm) and LM (average CH4 concentration: 1351.85 ± 205.20 ppm). Following CH4 measurement, on day 3, venous blood was sampled to determine the indicators of the metabolic status. HM cows had significantly higher average CH4 concentrations, maximum and average CH4 peak amplitude than LM cows in all measuring periods (P1-P3), while the number of CH4 peaks tended to be higher in HM than in LM cows in P2. There were no differences in the maximum and average CH4 peak width and average distance among two CH4 peaks between examined groups of cows. HM cows had significantly higher total protein concentrations and significantly lower total bilirubin and NEFA concentrations than LM cows. In conclusion, HM cows have a greater number of eructations and release more CH4 per eructation than LM cows, hence the differences in metabolic status are most likely related to the differences in their liver function.
Collapse
|
19
|
Invited Review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: implications for methane emissions in cattle. Livest Sci 2023. [DOI: 10.1016/j.livsci.2023.105171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
20
|
Hardan A, Garnsworthy PC, Bell MJ. Variability in Enteric Methane Emissions among Dairy Cows during Lactation. Animals (Basel) 2022; 13:ani13010157. [PMID: 36611765 PMCID: PMC9817987 DOI: 10.3390/ani13010157] [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: 11/28/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/04/2023] Open
Abstract
The aim of this study was to investigate variability in enteric CH4 emission rate and emissions per unit of milk across lactations among dairy cows on commercial farms in the UK. A total of 105,701 CH4 spot measurements were obtained from 2206 mostly Holstein-Friesian cows on 18 dairy farms using robotic milking stations. Eleven farms fed a partial mixed ration (PMR) and 7 farms fed a PMR with grazing. Methane concentrations (ppm) were measured using an infrared CH4 analyser at 1s intervals in breath samples taken during milking. Signal processing was used to detect CH4 eructation peaks, with maximum peak amplitude being used to derive CH4 emission rate (g/min) during each milking. A multiple-experiment meta-analysis model was used to assess effects of farm, week of lactation, parity, diet, and dry matter intake (DMI) on average CH4 emissions (expressed in g/min and g/kg milk) per individual cow. Estimated mean enteric CH4 emissions across the 18 farms was 0.38 (s.e. 0.01) g/min, ranging from 0.2 to 0.6 g/min, and 25.6 (s.e. 0.5) g/kg milk, ranging from 15 to 42 g/kg milk. Estimated dry matter intake was positively correlated with emission rate, which was higher in grazing cows, and negatively correlated with emissions per kg milk and was most significant in PMR-fed cows. Mean CH4 emission rate increased over the first 9 weeks of lactation and then was steady until week 70. Older cows were associated with lower emissions per minute and per kg milk. Rank correlation for CH4 emissions among weeks of lactation was generally high. We conclude that CH4 emissions appear to change across and within lactations, but ranking of a herd remains consistent, which is useful for obtaining CH4 spot measurements.
Collapse
Affiliation(s)
- Ali Hardan
- School of Biosciences, The University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
- Correspondence:
| | - Philip C. Garnsworthy
- School of Biosciences, The University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Matt J. Bell
- Animal and Agriculture Department, Hartpury University, Gloucester GL19 3BE, UK
| |
Collapse
|
21
|
Betancur-Murillo CL, Aguilar-Marín SB, Jovel J. Prevotella: A Key Player in Ruminal Metabolism. Microorganisms 2022; 11:microorganisms11010001. [PMID: 36677293 PMCID: PMC9866204 DOI: 10.3390/microorganisms11010001] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022] Open
Abstract
Ruminants are foregut fermenters that have the remarkable ability of converting plant polymers that are indigestible to humans into assimilable comestibles like meat and milk, which are cornerstones of human nutrition. Ruminants establish a symbiotic relationship with their microbiome, and the latter is the workhorse of carbohydrate fermentation. On the other hand, during carbohydrate fermentation, synthesis of propionate sequesters H, thus reducing its availability for the ultimate production of methane (CH4) by methanogenic archaea. Biochemically, methane is the simplest alkane and represents a downturn in energetic efficiency in ruminants; environmentally, it constitutes a potent greenhouse gas that negatively affects climate change. Prevotella is a very versatile microbe capable of processing a wide range of proteins and polysaccharides, and one of its fermentation products is propionate, a trait that appears conspicuous in P. ruminicola strain 23. Since propionate, but not acetate or butyrate, constitutes an H sink, propionate-producing microbes have the potential to reduce methane production. Accordingly, numerous studies suggest that members of the genus Prevotella have the ability to divert the hydrogen flow in glycolysis away from methanogenesis and in favor of propionic acid production. Intended for a broad audience in microbiology, our review summarizes the biochemistry of carbohydrate fermentation and subsequently discusses the evidence supporting the essential role of Prevotella in lignocellulose processing and its association with reduced methane emissions. We hope this article will serve as an introduction to novice Prevotella researchers and as an update to others more conversant with the topic.
Collapse
Affiliation(s)
- Claudia Lorena Betancur-Murillo
- Escuela de Ciencias Básicas, Tecnología e Ingeniería, Universidad Nacional Abierta y a Distancia, UNAD, Bogotá 111511, Colombia
| | | | - Juan Jovel
- Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada
- Correspondence:
| |
Collapse
|
22
|
Invited Review: Genetic decision tools for increasing cow efficiency and sustainability in forage-based beef systems. APPLIED ANIMAL SCIENCE 2022. [DOI: 10.15232/aas.2022-02306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
23
|
Strandén I, Kantanen J, Lidauer MH, Mehtiö T, Negussie E. Animal board invited review: Genomic-based improvement of cattle in response to climate change. Animal 2022; 16:100673. [PMID: 36402112 DOI: 10.1016/j.animal.2022.100673] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 12/24/2022] Open
Abstract
Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding.
Collapse
Affiliation(s)
- I Strandén
- Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland.
| | - J Kantanen
- Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
| | - M H Lidauer
- Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
| | - T Mehtiö
- Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
| | - E Negussie
- Natural Resources Institute Finland (Luke), 31600 Jokioinen, Finland
| |
Collapse
|
24
|
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]
|
25
|
Bačėninaitė D, Džermeikaitė K, Antanaitis R. Global Warming and Dairy Cattle: How to Control and Reduce Methane Emission. Animals (Basel) 2022; 12:2687. [PMID: 36230428 PMCID: PMC9559257 DOI: 10.3390/ani12192687] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/19/2022] [Accepted: 10/03/2022] [Indexed: 11/27/2022] Open
Abstract
Agriculture produces greenhouse gases. Methane is a result of manure degradation and microbial fermentation in the rumen. Reduced CH4 emissions will slow climate change and reduce greenhouse gas concentrations. This review compiled studies to evaluate the best ways to decrease methane emissions. Longer rumination times reduce methane emissions and milk methane. Other studies have not found this. Increasing propionate and reducing acetate and butyrate in the rumen can reduce hydrogen equivalents that would otherwise be transferred to methanogenesis. Diet can reduce methane emissions. Grain lowers rumen pH, increases propionate production, and decreases CH4 yield. Methane generation per unit of energy-corrected milk yield reduces with a higher-energy diet. Bioactive bromoform discovered in the red seaweed Asparagopsis taxiformis reduces livestock intestinal methane output by inhibiting its production. Essential oils, tannins, saponins, and flavonoids are anti-methanogenic. While it is true that plant extracts can assist in reducing methane emissions, it is crucial to remember to source and produce plants in a sustainable manner. Minimal lipid supplementation can reduce methane output by 20%, increasing energy density and animal productivity. Selecting low- CH4 cows may lower GHG emissions. These findings can lead to additional research to completely understand the impacts of methanogenesis suppression on rumen fermentation and post-absorptive metabolism, which could improve animal productivity and efficiency.
Collapse
Affiliation(s)
- Dovilė Bačėninaitė
- Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, LT-47181 Kaunas, Lithuania
| | | | | |
Collapse
|
26
|
Beauchemin KA, Ungerfeld EM, Abdalla AL, Alvarez C, Arndt C, Becquet P, Benchaar C, Berndt A, Mauricio RM, McAllister TA, Oyhantçabal W, Salami SA, Shalloo L, Sun Y, Tricarico J, Uwizeye A, De Camillis C, Bernoux M, Robinson T, Kebreab E. Invited review: Current enteric methane mitigation options. J Dairy Sci 2022; 105:9297-9326. [DOI: 10.3168/jds.2022-22091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/23/2022] [Indexed: 11/06/2022]
|
27
|
Prediction of dry matter intake and gross feed efficiency using milk production and live weight in first-parity Holstein cows. Trop Anim Health Prod 2022; 54:278. [PMID: 36074215 DOI: 10.1007/s11250-022-03275-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/31/2022] [Indexed: 10/14/2022]
Abstract
Direct measurement of dry matter intake (DMI) presents a major challenge in estimating gross feed efficiency (GFE) in dairy cattle. This challenge can, however, be resolved through the prediction of DMI and GFE from easy-to-measure traits such as milk production (i.e. milk yield, energy-corrected milk (ECM), butterfat, protein, lactose) and live weight (LW). The main objective of this study was, therefore, to investigate the feasibility of predicting dry matter intake and gross feed efficiency for first-parity Holstein cows using milk production traits and LW. Data comprised of 30 daily measurements of DMI and milk production traits, and 25 daily LW records of a group of 100 first-parity Holstein cows, fed a total mixed ration. Gross feed efficiency was calculated as kg ECM divided by kg DMI. The initial step was to estimate correlations of milk production traits and LW with DMI and GFE, to identify the best potential predictors of DMI and GFE. Subsequently, a forward stepwise regression analysis was used to develop models to predict DMI and GFE from LW and milk production traits, followed by within-herd validations. Means for DMI, butterfat yield (BFY) and LW were 21.91 ± 2.77 kg/day, 0.95 ± 0.14 kg/day and 572 ± 15.58 kg/day, respectively. Mean GFE was 1.32 ± 0.22. Dry matter intake had positive correlations with milk yield (MY) (r = 0.32, p < 0.001) and LW (r = 0.76, p < 0.0001) and an antagonistic association with butterfat percent (BFP) (r = - 0.55, p < 0.001). On the other hand, GFE was positively associated with MY (r = 0.36, p < 0.001), BFP (r = 0.53, p < 0.001) and BFY (r = 0.83, p < 0.0001), and negatively correlated with LW (r = - 0.23, p > 0.05). Dry matter intake was predicted reliably by a model comprising of only LW and MY (R2 = 0.79; root mean squared error (RMSE) = 1.05 kg/day). A model that included BFY, MY and LW had the highest ability to predict GFE (R2 = 0.98; RMSE = 0.05). Live weight and BFY were the main predictor traits for DMI and GFE, respectively. The best models for predicting DMI and GFE were as follows: DMI (kg/day) = - 54.21 - 0.192 × MY (kg/day) + 0.146 × LW (kg/day) and GFE (kg/day) = 4.120 + 0.024 × MY (kg/day) + 1.000 × BFY (kg/day) - 0.008 × LW (kg/day). Thus, daily DMI (kg/day) and GFE can be reliably predicted from LW and milk production traits using these developed models in first-parity Holstein cows. This presents a big promise to generate large quantities of data of individual cow DMI and GFE, which can be used to implement genetic improvement of feed efficiency.
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
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]
|
30
|
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] [Key Words] [MESH Headings] [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.
Collapse
Affiliation(s)
- Navid Ghavi Hossein-Zadeh
- Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, 41635-1314, Iran.
| |
Collapse
|
31
|
Ruiz-Llontop D, Velarde-Guillén J, Fuentes E, Prudencio M, Gómez C. Milk carbon footprint of silvopastoral dairy systems in the Northern Peruvian Amazon. Trop Anim Health Prod 2022; 54:227. [PMID: 35809110 DOI: 10.1007/s11250-022-03224-5] [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: 01/18/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022]
Abstract
The objective of this study was to estimate the carbon footprint (CF) of milk production (in kg of CO2 equivalents (CO2e) per kg of fat and protein corrected milk (FPCM)) in dairy farms of the San Martín region, in the Peruvian Amazon. A cradle-to-farm gate characterization and analysis were carried out on eight representative dairy farms. Greenhouse gas (GHG) emissions were estimated using equations, following the 2019 refinement of the 2006 IPCC Guidelines. The results showed an average milk production of 9.7 ± 0.82 L milk/cow/day, Gyr x Holstein crosses as the predominant breed, use of cultivated grasses such as Brachiaria brizantha, living fences (Guazuma ulmifolia Lam) as the predominant silvopastoral arrangement, and low level of external inputs such as feed or grain additives. In relation to CF, an average value of 2.26 ± 0.49 kg CO2e/kg FPCM was obtained, with enteric fermentation being the most important source (1.81 ± 0.51 kg CO2e/kg FPCM), followed by manure management, land use, and energy/transport (0.26 ± 0.06, 0.14 ± 0.04, and 0.05 ± 0.04 kg CO2e/kg FPCM, respectively). Differences were found between farmers, obtaining lower CF values (1.76 vs 3.09 kg CO2e/kg FPCM) on farms with better feed quality, higher production levels, and a higher percentage of lactating animals compared to dry cows. It is concluded that dairy farms in the Peruvian Amazon region can reduce their emissions if they improve their current feeding practices.
Collapse
Affiliation(s)
- Deysi Ruiz-Llontop
- Universidad Nacional Agraria La Molina, Av. La Molina s/n, La Molina, Lima, Peru
| | - José Velarde-Guillén
- Universidad Nacional Agraria La Molina, Av. La Molina s/n, La Molina, Lima, Peru
| | - Eduardo Fuentes
- Universidad Nacional Agraria La Molina, Av. La Molina s/n, La Molina, Lima, Peru
| | - Melisa Prudencio
- Universidad Nacional Agraria La Molina, Av. La Molina s/n, La Molina, Lima, Peru
| | - Carlos Gómez
- Universidad Nacional Agraria La Molina, Av. La Molina s/n, La Molina, Lima, Peru.
| |
Collapse
|
32
|
Host genetics associated with gut microbiota and methane emission in cattle. Mol Biol Rep 2022; 49:8153-8161. [PMID: 35776394 DOI: 10.1007/s11033-022-07718-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/15/2022] [Indexed: 10/17/2022]
Abstract
In livestock sector, dairy animals alone produce 18% of the total greenhouse gas emissions globally as methane (CH4). This Enteric methane is the largest component of total carbon footprints produced by livestock production system and its reduction is today's new challenge to make livestock farming sustainable for earth's environment. The production of enteric methane in ruminants is a complex phenomena involving different host factors like host genotype, rumen microbiome, host physiology along with dietary factors. Efforts have been made to reduce methane emissions largely through nutritional interventions and dietary supplements, but permanent reductions can be obtained through genetic means by selecting and breeding of low methane emitting animals. From genome-wide association studies, many important genomic QTL regions and single nucleotide polymorphisms involved in shaping the composition of the ruminal microbiome and thus their carbon footprints have been recognised, implying that methane emission traits are quantitative traits. The major bottleneck in implementation of reduced methane emission traits in the breeding programs is wide variation at phenotypic level, lack of precise methane measurements at individual level. Overall, the heritability for CH4 production traits is moderate, and it can be used in breeding programmes to target changes in microbial composition to reduce CH4 emission in the dairy industry for far-reaching environmental benefits at the cost of a minor reduction in genetic gain in production traits.
Collapse
|
33
|
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: 15] [Impact Index Per Article: 7.5] [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.
Collapse
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
| |
Collapse
|
34
|
van Breukelen AE, Aldridge MA, Veerkamp RF, de Haas Y. Genetic parameters for repeatedly recorded enteric methane concentrations of dairy cows. J Dairy Sci 2022; 105:4256-4271. [PMID: 35307185 DOI: 10.3168/jds.2021-21420] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/08/2022] [Indexed: 11/19/2022]
Abstract
Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH4) concentrations, using the largest data set from long-term repeatedly recorded CH4 on cows to date, and to evaluate (1) the accuracy of breeding values for different CH4 traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH4 and carbon dioxide (CO2), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH4 and CO2 concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH4/CO2 ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH4 per visit (0.13) but lower for logCH4 and CH4/CO2 (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH4 traits, apart from the genetic correlations with the CH4/CO2 trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH4, measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH4 (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations.
Collapse
Affiliation(s)
- A E van Breukelen
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands.
| | - M A Aldridge
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - R F Veerkamp
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Y de Haas
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| |
Collapse
|
35
|
Løvendahl P, Buitenhuis A. Genetic and phenotypic variation and consistency in cow preference and circadian use of robotic milking units. J Dairy Sci 2022; 105:5283-5295. [DOI: 10.3168/jds.2021-21593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/15/2022] [Indexed: 11/19/2022]
|
36
|
Burns JG, Glenk K, Eory V, Simm G, Wall E. Preferences of European dairy stakeholders in breeding for resilient and efficient cattle: A best-worst scaling approach. J Dairy Sci 2021; 105:1265-1280. [PMID: 34955264 DOI: 10.3168/jds.2021-20316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 10/16/2021] [Indexed: 12/21/2022]
Abstract
Including resilience in the breeding objective of dairy cattle is gaining increasing attention, primarily as anticipated challenges to production systems, such as climate change, may make some perturbations more difficult to moderate at the farm level. Consequently, the underlying biological mechanisms by which resilience is achieved are likely to become an important part of the system itself, increasing value on the animal's ability to be unperturbed by variable production circumstances, or to quickly return to pre-perturbed levels of productivity and health. However, because the value of improving genetic traits to a system is usually based on known profit functions or bioeconomic models linked to current production conditions, it can be difficult to define longer-term value, especially under uncertain future production circumstances and where nonmonetary values may be progressively more important. We present the novel application of a discrete choice experiment, used to investigate potential antagonisms in the values of genetic improvements for 8 traits to dairy cattle system stakeholders in Europe when the production goal was either efficiency or resilience. A latent class model was used to identify heterogeneous preferences within each production goal, and postestimation was used to identify associations between these preferences and sociodemographic characteristics of respondents. Results suggested 3 distinct latent preference classes for each production goal. For the efficiency goal, yield and feed efficiency traits were generally highly valued, whereas for the resilience goal, health and robustness traits were generally highly valued. In both cases, these traits generally carried a low value in the other production scenario. Overall, in both scenarios, longevity was highly valued; however, the value of this trait in terms of resilience will depend on phenotyping across diverse environments to sufficiently capture performance under various anticipated system challenges. Additionally, results showed significant associations between membership of latent preference classes with education level and profession. In conclusion, as resilience becomes increasingly important, it is likely that a continued reliance on the short-term economic value of traits alone will lead decision makers to misrepresent the importance of some traits, including those with substantial contextual values in terms of resilience.
Collapse
Affiliation(s)
- J G Burns
- Global Academy of Agriculture and Food Security, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, United Kingdom; Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom.
| | - K Glenk
- Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom
| | - V Eory
- Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom
| | - G Simm
- Global Academy of Agriculture and Food Security, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, United Kingdom
| | - E Wall
- Scotland's Rural College (SRUC), Peter Wilson Building, King's Buildings, Edinburgh, EH9 3JG, United Kingdom
| |
Collapse
|
37
|
Detection of Methane Eructation Peaks in Dairy Cows at a Robotic Milking Station Using Signal Processing. Animals (Basel) 2021; 12:ani12010026. [PMID: 35011131 PMCID: PMC8749638 DOI: 10.3390/ani12010026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/16/2021] [Accepted: 12/21/2021] [Indexed: 11/24/2022] Open
Abstract
Simple Summary The objective of this study was to investigate the use of signal processing to detect eructation peaks in methane (CH4) released by dairy cows during robotic milking using three gas analysers. This study showed that signal processing can be used to detect CH4 eructations and extract spot measurements from individual cows whilst being milked. There was a reasonable correlation between the gas analysers studied. Measurement of eructations using a signal processing approach can provide a repeatable and accurate measurement of enteric CH4 emissions from cows with different gas analysers. Abstract The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH4 released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH4 in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH4 amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH4 spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH4 concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH4 concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH4 eructations from animals when using different gas analysers.
Collapse
|
38
|
Manzanilla-Pech CIV, Difford GF, Sahana G, Romé H, Løvendahl P, Lassen J. Genome-wide association study for methane emission traits in Danish Holstein cattle. J Dairy Sci 2021; 105:1357-1368. [PMID: 34799107 DOI: 10.3168/jds.2021-20410] [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: 03/05/2021] [Accepted: 10/07/2021] [Indexed: 02/04/2023]
Abstract
Selecting for lower methane emitting cows requires insight into the most biologically relevant phenotypes for methane emission, which are close to the breeding goal. Several methane phenotypes have been suggested over the last decade. However, the (dis)similarity of their underlying genetic architecture and correlation structures are poorly understood. Therefore, the objective of this study was to test association of SNP and genomic regions through GWAS on 8 CH4 emission traits in Danish Holstein cattle. The traits studied were methane concentration (MeC; ppm), methane production (MeP ; g/d), 2 definitions of residual methane (RMETc and RMETp: MeC and MeP regressed on metabolic body weight and energy-corrected milk, respectively), 2 definitions of methane intensity (MeI; MeIc = MeC/ECM and MeIp = MeP/ECM); 2 definitions of methane yield per kilogram of dry matter intake (MeY; MeYc = MeC/dry matter intake and MeYp = MeP/dry matter intake). A total of 1,962 cows with genotypes (Illumina BovineSNP50 Chip or Eurogenomic custom SNP chip) and repeated records of the above-mentioned 8 methane traits were analyzed. Strong associations were found with 3 traits (MeC, MeP, and MeYc) on chromosome 13 and with 5 traits (MeC, MeP, MeIp, MeYp, and MeYc) on chromosome 26. For MeIc, MeIp, RMETc, MeYc, and MeYp, some suggestive association signals were identified on chromosome 1. Genomic segments of 1 Mbp (n = 2,525) were tested for their association with these traits, which identified between 33 to 54 significantly associated regions. In a pairwise comparison, MeC and MeP were the traits that shared the highest number of significant segments (17). The same trend was observed when comparing SNP significantly associated with the traits MeC and MeP shared from 23 to 25 SNP (most of which were located in chromosomes 11, 13, and 26). Based on our results on GWAS and genetic correlations, we conclude that MeC is (genetically) more closely linked to MeP than any of the other methane traits analyzed.
Collapse
Affiliation(s)
- C I V Manzanilla-Pech
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark.
| | - G F Difford
- Department of Breeding and Genetics, Nofima AS, PO Box 210, N-1431 Ås, Norway
| | - G Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - H Romé
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - P Løvendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - J Lassen
- Viking Genetics, Ebeltoftvej 16, Assentoft, 8960 Randers, Denmark
| |
Collapse
|
39
|
Sypniewski M, Strabel T, Pszczola M. Genetic Variability of Methane Production and Concentration Measured in the Breath of Polish Holstein-Friesian Cattle. Animals (Basel) 2021; 11:ani11113175. [PMID: 34827907 PMCID: PMC8614515 DOI: 10.3390/ani11113175] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 11/16/2022] Open
Abstract
The genetic architecture of methane (CH4) production remains largely unknown. We aimed to estimate its heritability and to perform genome-wide association studies (GWAS) for the identification of candidate genes associated with two phenotypes: CH4 in parts per million/day (CH4 ppm/d) and CH4 in grams/day (CH4 g/d). We studied 483 Polish Holstein-Friesian cows kept on two commercial farms in Poland. Measurements of CH4 and carbon dioxide (CO2) concentrations exhaled by cows during milking were obtained using gas analyzers installed in the automated milking system on the farms. Genomic analyses were performed using a single-step BLUP approach. The percentage of genetic variance explained by SNPs was calculated for each SNP separately and then for the windows of neighbouring SNPs. The heritability of CH4 ppm/d ranged from 0 to 0.14, with an average of 0.085. The heritability of CH4 g/d ranged from 0.13 to 0.26, with an average of 0.22. The GWAS detected potential candidate SNPs on BTA 14 which explained ~0.9% of genetic variance for CH4 ppm/d and ~1% of genetic variance for CH4 g/d. All identified SNPs were located in the TRPS1 gene. We showed that methane traits are partially controlled by genes; however, the detected SNPs explained only a small part of genetic variation-implying that both CH4 ppm/d and CH4 g/d are highly polygenic traits.
Collapse
|
40
|
Suzuki T, Kamiya Y, Oikawa K, Nonaka I, Shinkai T, Terada F, Obitsu T. Prediction of enteric methane emissions from lactating cows using methane to carbon dioxide ratio in the breath. Anim Sci J 2021; 92:e13637. [PMID: 34592786 PMCID: PMC9285552 DOI: 10.1111/asj.13637] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/19/2021] [Accepted: 08/12/2021] [Indexed: 11/28/2022]
Abstract
The aim of this study was to develop prediction equations for methane (CH4) emissions from lactating cows using the CH4/carbon dioxide (CO2) ratio in the breath measured in the automatic milking system (AMS) and to evaluate the predicted values and factors affecting the CH4/CO2 ratio. The model development was conducted using a dataset determined in respiration chambers or head boxes (n = 121). Then, gas measurements in the AMS as well as in the head box were carried out with six lactating cows fed one of three different levels of neutral detergent fiber (NDF) content, following a 3 × 3 Latin square experimental design. The obtained equation that is suitable for practical use on farms to predict CH4 was CH4 (L/day) = −507 + 0.536 live weight (kg) + 8.76 energy‐corrected milk (kg/day) + 5,029 CH4/CO2 (adjusted R2 = 0.83; root mean square error = 40.8 L/day). Results showed that the predicted values correlated positively with the observed values, the determined CH4/CO2 ratio increased with increasing dietary NDF content, and the detected eructation rate was in the normal range. On the other hand, the CH4/CO2 ratio was affected by the time interval between measurement and last eating before the measurement.
Collapse
Affiliation(s)
- Tomoyuki Suzuki
- Institute of Livestock and Grassland Science, NARO, Nasushiobara, Japan
| | - Yuko Kamiya
- Institute of Livestock and Grassland Science, NARO, Nasushiobara, Japan
| | - Kohei Oikawa
- Institute of Livestock and Grassland Science, NARO, Nasushiobara, Japan
| | - Itoko Nonaka
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | - Takumi Shinkai
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | - Fuminori Terada
- Institute of Livestock and Grassland Science, NARO, Tsukuba, Japan
| | - Taketo Obitsu
- Graduate School of Biosphere Science, Hiroshima University, Higashihiroshima, Japan
| |
Collapse
|
41
|
Genome-wise engineering of ruminant nutrition- nutrigenomics: applications, challenges, and future perspectives – a review. ANNALS OF ANIMAL SCIENCE 2021. [DOI: 10.2478/aoas-2021-0057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Abstract
Use of genomic information in ruminant production systems can help relieve concerns related to food security and sustainability of production. Nutritional genomics (i.e., Nutrigenomics) is a field of research that is interested in all types of reciprocal interactions between nutrients and genomes of organisms, i.e., variable patterns of gene expression and effect of genetic variations on the nutritional environment. Devising a revolutionizing analytical approach to traditional ruminant nutrition research, the relatively novel area of ruminant nutrigenomics has several studies concerning different aspects of animal production systems. This paper aims to review the current nutrigenomics research in the frame of how nutrition of ruminants can be modified accounting for individual genetic backgrounds and gene/diet relationships behind productivity, quality, efficiency, disease resistance, fertility, and GHG emissions. Furthermore, current challenges facing ruminant nutrigenomics are evaluated and future directions for the novel area are strongly argued by this review.
Collapse
|
42
|
Zhang X, Amer PR, Stachowicz K, Quinton C, Crowley J. Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle. Animal 2021; 15:100325. [PMID: 34371470 DOI: 10.1016/j.animal.2021.100325] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 06/29/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022] Open
Abstract
In response to the increased concern over agriculture's contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH4) per cow per year after accounting for replacement costs, ranging 8.9-323 kg CH4/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.
Collapse
Affiliation(s)
- X Zhang
- AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand
| | - P R Amer
- AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand.
| | - K Stachowicz
- AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand
| | - C Quinton
- AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand
| | - J Crowley
- AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand
| |
Collapse
|
43
|
de Haas Y, Veerkamp RF, de Jong G, Aldridge MN. Selective breeding as a mitigation tool for methane emissions from dairy cattle. Animal 2021; 15 Suppl 1:100294. [PMID: 34246599 DOI: 10.1016/j.animal.2021.100294] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 12/17/2022] Open
Abstract
The global livestock sector, particularly ruminants, contributes substantially to the total anthropogenic greenhouse gases. Management and dietary solutions to reduce enteric methane (CH4) emissions are extensively researched. Animal breeding that exploits natural variation in CH4 emissions is an additional mitigation solution that is cost-effective, permanent, and cumulative. We quantified the effect of including CH4 production in the Dutch breeding goal using selection index theory. The current Dutch national index contains 15 traits, related to milk yield, longevity, health, fertility, conformation and feed efficiency. From the literature, we obtained a heritability of 0.21 for enteric CH4 production, and genetic correlations of 0.4 with milk lactose, protein, fat and DM intake. Correlations between enteric CH4 production and other traits in the breeding goal were set to zero. When including CH4 production in the current breeding goal with a zero economic value, CH4 production increases each year by 1.5 g/d as a correlated response. When extrapolating this, the average daily CH4 production of 392 g/d in 2018 will increase to 442 g/d in 2050 (+13%). However, expressing the CH4 production as CH4 intensity in the same period shows a reduction of 13%. By putting economic weight on CH4 production in the breeding goal, selective breeding can reduce the CH4 intensity even by 24% in 2050. This shows that breeding is a valuable contribution to the whole set of mitigation strategies that could be applied in order to achieve the goals for 2050 set by the EU. If the decision is made to implement animal breeding strategies to reduce enteric CH4 production, and to achieve the expected breeding impact, there needs to be a sufficient reliability of prediction. The only way to achieve that is to have enough animals phenotyped and genotyped. The power calculations offer insights into the difficulties that will be faced in trying to record enough data. Recording CH4 data on 100 farms (with on average 150 cows each) for at least 2 years is required to achieve the desired reliability of 0.40 for the genomic prediction.
Collapse
Affiliation(s)
- Y de Haas
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands.
| | - R F Veerkamp
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - G de Jong
- CRV, 6800 AL Arnhem, the Netherlands
| | - M N Aldridge
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| |
Collapse
|
44
|
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.
Collapse
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
| |
Collapse
|
45
|
Modelling the Distribution of the Red Macroalgae Asparagopsis to Support Sustainable Aquaculture Development. AGRIENGINEERING 2021. [DOI: 10.3390/agriengineering3020017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Fermentative digestion by ruminant livestock is one of the main ways enteric methane enters the atmosphere, although recent studies have identified that including red macroalgae as a feed ingredient can drastically reduce methane produced by cattle. Here, we utilize ecological modelling to identify suitable sites for establishing aquaculture development to support sustainable agriculture and Sustainable Development Goals 1 and 2. We used species distributions models (SDMs) parameterized using an ensemble of multiple statistical and machine learning methods, accounting for novel methodological and ecological artefacts that arise from using such approaches on non-native and cultivated species. We predicted the current distribution of two Asparagopsis species to high accuracy around the coast of Ireland. The environmental drivers of each species differed depending on where the response data was sourced from (i.e., native vs. non-native), suggesting that the length of time A. armata has been present in Ireland may mean it has undergone a niche shift. Subsequently, researchers looking to adopt SDMs to support aquaculture development need to acknowledge emerging conceptual issues, and here we provide the code needed to implement such research, which should support efforts to effectively choose suitable sites for aquaculture development that account for the unique methodological steps identified in this research.
Collapse
|
46
|
Lean IJ, Moate PJ. Cattle, climate and complexity: food security, quality and sustainability of the Australian cattle industries. Aust Vet J 2021; 99:293-308. [PMID: 33973228 DOI: 10.1111/avj.13072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/31/2021] [Accepted: 04/02/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Marked increases in atmospheric CO2 concentrations are largely associated with the release of sequestered carbon in fossil fuels. While emissions of green-house gasses (GHG) from cattle have significant global warming potential, these are biogenic sources and substantially involve carbon in natural cycles, rather than fossil fuel. Cattle use human inedible feeds and by-products of human food production to produce nutrient-dense foods of great value to humans. INTERVENTIONS TO REDUCE GHG PRODUCTION Reductions in land clearing and burning of grasslands and increased carbon sequestration in soils and trees have potential to substantially reduce GHG emissions. Increased efficiencies of production through intensified feeding and enteric modification have markedly reduced intensity of GHG emissions for cattle in Australia. Genetic selection for lower emissions has modest, but cumulative potential to reduce GHG (mostly CH4 ) emissions and intensity. Improved reproductive performance can reduce intensity of GHG emissions, especially in beef production. Feeds and technologies that reduce GHG production and intensity include improved pastures, grain feeding, dietary lipids, nitrates, ionophores, seaweed, 3-NOP, hormonal growth promotants in beef, and improved diets for peri-parturient dairy cattle. There is considerable potential to further reduce emissions from cattle using the technologies reviewed. INTERVENTIONS TO REDUCE HEAT STRESS Cattle are susceptible to heat stress and ameliorating interventions include tree and shelter belts, shade, housing, cooling with fans and water and dietary manipulations. CONCLUSIONS Numerous interventions can reduce GHG emissions and intensity from cattle. There are opportunities to increase carbon capture and maintain biodiversity in Australia's extensive rangelands, but these require quantification and application. We can reduce the intensity of CH4 emissions for cattle in Australia and simultaneously improve their well-being.
Collapse
Affiliation(s)
- I J Lean
- Scibus and University of Sydney, Camden, New South Wales, 2570, Australia
| | - P J Moate
- Agriculture Victoria, Ellinbank, Victoria, 3821, Australia
| |
Collapse
|
47
|
Silpa MV, König S, Sejian V, Malik PK, Nair MRR, Fonseca VFC, Maia ASC, Bhatta R. Climate-Resilient Dairy Cattle Production: Applications of Genomic Tools and Statistical Models. Front Vet Sci 2021; 8:625189. [PMID: 33996959 PMCID: PMC8117237 DOI: 10.3389/fvets.2021.625189] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/15/2021] [Indexed: 01/02/2023] Open
Abstract
The current changing climate trend poses a threat to the productive efficacy and welfare of livestock across the globe. This review is an attempt to synthesize information pertaining to the applications of various genomic tools and statistical models that are available to identify climate-resilient dairy cows. The different functional and economical traits which govern milk production play a significant role in determining the cost of milk production. Thus, identification of these traits may revolutionize the breeding programs to develop climate-resilient dairy cattle. Moreover, the genotype–environment interaction also influences the performance of dairy cattle especially during a challenging situation. The recent advancement in molecular biology has led to the development of a few biotechnological tools and statistical models like next-generation sequencing (NGS), microarray technology, whole transcriptome analysis, and genome-wide association studies (GWAS) which can be used to quantify the molecular mechanisms which govern the climate resilience capacity of dairy cows. Among these, the most preferred option for researchers around the globe was GWAS as this approach jointly takes into account all the genotype, phenotype, and pedigree information of farm animals. Furthermore, selection signatures can also help to demarcate functionally important regions in the genome which can be used to detect potential loci and candidate genes that have undergone positive selection in complex milk production traits of dairy cattle. These identified biomarkers can be incorporated in the existing breeding policies using genomic selection to develop climate-resilient dairy cattle.
Collapse
Affiliation(s)
- Mullakkalparambil Velayudhan Silpa
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität Gießen, Gießen, Germany.,Center for Climate Resilient Animal Adaptation Studies, Indian Council of Agricultural Research-National Institute of Animal Nutrition and Physiology, Bangalore, India
| | - Sven König
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität Gießen, Gießen, Germany
| | - Veerasamy Sejian
- Center for Climate Resilient Animal Adaptation Studies, Indian Council of Agricultural Research-National Institute of Animal Nutrition and Physiology, Bangalore, India
| | - Pradeep Kumar Malik
- Center for Climate Resilient Animal Adaptation Studies, Indian Council of Agricultural Research-National Institute of Animal Nutrition and Physiology, Bangalore, India
| | - Mini Ravi Reshma Nair
- Center for Climate Resilient Animal Adaptation Studies, Indian Council of Agricultural Research-National Institute of Animal Nutrition and Physiology, Bangalore, India
| | - Vinicius F C Fonseca
- Innovation Group of Thermal Comfort and Animal Welfare (INOBIO-MANERA), Animal Science Department, Universidade Federal da Paraíba, Areia, Brazil.,Brain Function Research Group, Faculty of Health Sciences, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa
| | - Alex Sandro Campos Maia
- Innovation Group of Thermal Comfort and Animal Welfare (INOBIO-MANERA), Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista (Unesp), São Paulo, Brazil
| | - Raghavendra Bhatta
- Center for Climate Resilient Animal Adaptation Studies, Indian Council of Agricultural Research-National Institute of Animal Nutrition and Physiology, Bangalore, India
| |
Collapse
|
48
|
Zhu Z, Difford GF, Noel SJ, Lassen J, Løvendahl P, Højberg O. Stability Assessment of the Rumen Bacterial and Archaeal Communities in Dairy Cows Within a Single Lactation and Its Association With Host Phenotype. Front Microbiol 2021; 12:636223. [PMID: 33927700 PMCID: PMC8076905 DOI: 10.3389/fmicb.2021.636223] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/28/2021] [Indexed: 01/09/2023] Open
Abstract
Better characterization of changes in the rumen microbiota in dairy cows over the lactation period is crucial for understanding how microbial factors may potentially be interacting with host phenotypes. In the present study, we characterized the rumen bacterial and archaeal community composition of 60 lactating Holstein dairy cows (33 multiparous and 27 primiparous), sampled twice within the same lactation with a 122 days interval. Firmicutes and Bacteroidetes dominated the rumen bacterial community and showed no difference in relative abundance between samplings. Two less abundant bacterial phyla (SR1 and Proteobacteria) and an archaeal order (Methanosarcinales), on the other hand, decreased significantly from the mid-lactation to the late-lactation period. Moreover, between-sampling stability assessment of individual operational taxonomic units (OTUs), evaluated by concordance correlation coefficient (C-value) analysis, revealed the majority of the bacterial OTUs (6,187 out of 6,363) and all the 79 archaeal OTUs to be unstable over the investigated lactation period. The remaining 176 stable bacterial OTUs were mainly assigned to Prevotella, unclassified Prevotellaceae, and unclassified Bacteroidales. Milk phenotype-based screening analysis detected 32 bacterial OTUs, mainly assigned to unclassified Bacteroidetes and Lachnospiraceae, associated with milk fat percentage, and 6 OTUs, assigned to Ruminococcus and unclassified Ruminococcaceae, associated with milk protein percentage. These OTUs were only observed in the multiparous cows. None of the archaeal OTUs was observed to be associated with the investigated phenotypic parameters, including methane production. Co-occurrence analysis of the rumen bacterial and archaeal communities revealed Fibrobacter to be positively correlated with the archaeal genus vadinCA11 (Pearson r = 0.76) and unclassified Methanomassiliicoccaceae (Pearson r = 0.64); vadinCA11, on the other hand, was negatively correlated with Methanobrevibacter (Pearson r = –0.56). In conclusion, the rumen bacterial and archaeal communities of dairy cows displayed distinct stability at different taxonomic levels. Moreover, specific members of the rumen bacterial community were observed to be associated with milk phenotype parameters, however, only in multiparous cows, indicating that dairy cow parity could be one of the driving factors for host–microbe interactions.
Collapse
Affiliation(s)
- Zhigang Zhu
- Department of Animal Science, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark
| | - Gareth Frank Difford
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark
| | - Samantha Joan Noel
- Department of Animal Science, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark
| | - Jan Lassen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark
| | - Peter Løvendahl
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark
| | - Ole Højberg
- Department of Animal Science, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark
| |
Collapse
|
49
|
Celis-Alvarez MD, López-González F, Arriaga-Jordán CM, Robles-Jiménez LE, González-Ronquillo M. Feeding Forage Mixtures of Ryegrass ( Lolium spp.) with Clover ( Trifolium spp.) Supplemented with Local Feed Diets to Reduce Enteric Methane Emission Efficiency in Small-Scale Dairy Systems: A Simulated Study. Animals (Basel) 2021; 11:ani11040946. [PMID: 33801732 PMCID: PMC8067253 DOI: 10.3390/ani11040946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The present study simulated the effects of different dairy cow diets based on local feeding strategies on enteric methane (CH4) emissions and surpluses of crude protein (CP) in small-scale dairy systems (SSDS). Our study evaluated five scenarios of supplementation (S): without supplementation (control diet), meaning no supplements were provided, only pasture (S1); pasture supplemented with 4.5 kg dry matter (DM)/cow/day of commercial concentrate (CC) (S2); supplemented with 200 g DM/kg per milk produced of CC (S3); supplemented with ground maize grains and wet distiller brewery grains (S4); and S4 plus maize silage (S5). In addition, two pasture managements (cut-and-carry versus grazing) and two varieties of legumes (red clover vs. white clover) were considered. The results suggest that methane emissions and large nitrogen surpluses in the diet are affected by the type of supplementation given to cows, in addition to the management and chemical composition of the pastures offered. In SSDS, it is possible to formulate diets with local inputs to reduce excess nutrients and dependence on external inputs, increasing feed efficiency and reducing costs (excess of CP in the diet) and CH4 emissions. Abstract In cattle, greenhouse gas (GHG) emissions and nutrient balance are influenced by factors such as diet composition, intake, and digestibility. This study evaluated CH4 emissions and surpluses of crude protein, using five simulated scenarios of supplementation in small-scale dairy systems (SSDS). In addition, two pasture managements (cut-and-carry versus grazing) and two varieties of legumes (red clover vs. white clover) were considered. The diets were tested considering similar milk yield and chemical composition; CH4 emission was estimated using Tier-2 methodology from the Intergovernmental Panel on Climate Change (IPCC), and the data were analyzed in a completely randomized 5 × 2 × 2 factorial design. Differences (p < 0.05) were found in predicted CH4 emissions per kg of milk produced (g kg−1 FCM 3.5%). The lowest predicted CH4 emissions were found for S3 and S4 as well as for pastures containing white clover. Lower dietary surpluses of CP (p < 0.05) were observed for the control diet (1320 g CP/d), followed by S5 (1793 g CP/d), compared with S2 (2175 g CP/d), as well as in cut-and-carry management with red clover. A significant correlation (p < 0.001) was observed between dry matter intake and CH4 emissions (g−1 and per kg of milk produced). It is concluded that the environmental impact of formulating diets from local inputs (S3 and S4) can be reduced by making them more efficient in terms of methane kg−1 of milk in SSDS.
Collapse
Affiliation(s)
- Maria Danaee Celis-Alvarez
- Instituto de Ciencias Agropecuarias y Rurales, Universidad Autónoma del Estado de México, No. 100 Instituto Literario, Toluca 50000, Estado de México, Mexico; (M.D.C.-A.); (C.M.A.-J.)
| | - Felipe López-González
- Instituto de Ciencias Agropecuarias y Rurales, Universidad Autónoma del Estado de México, No. 100 Instituto Literario, Toluca 50000, Estado de México, Mexico; (M.D.C.-A.); (C.M.A.-J.)
- Correspondence: (F.L.-G.); (M.G.-R.)
| | - Carlos Manuel Arriaga-Jordán
- Instituto de Ciencias Agropecuarias y Rurales, Universidad Autónoma del Estado de México, No. 100 Instituto Literario, Toluca 50000, Estado de México, Mexico; (M.D.C.-A.); (C.M.A.-J.)
| | - Lizbeth E. Robles-Jiménez
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, No. 100 Instituto Literario 100, Col. Centro, Toluca 50000, Estado de México, Mexico;
| | - Manuel González-Ronquillo
- Facultad de Medicina Veterinaria y Zootecnia, Universidad Autónoma del Estado de México, No. 100 Instituto Literario 100, Col. Centro, Toluca 50000, Estado de México, Mexico;
- Correspondence: (F.L.-G.); (M.G.-R.)
| |
Collapse
|
50
|
Pérez-Enciso M, Steibel JP. Phenomes: the current frontier in animal breeding. Genet Sel Evol 2021; 53:22. [PMID: 33673800 PMCID: PMC7934239 DOI: 10.1186/s12711-021-00618-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/22/2021] [Indexed: 12/13/2022] Open
Abstract
Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and ‘missingness’ of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.
Collapse
Affiliation(s)
- Miguel Pérez-Enciso
- ICREA, Passeig de Lluís Companys 23, 08010, Barcelona, Spain. .,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Bellaterra, 08193, Barcelona, Spain.
| | - Juan P Steibel
- Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA.,Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA
| |
Collapse
|