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Rodrigues ARF, Silva ME, Silva VF, Maia MRG, Cabrita ARJ, Trindade H, Fonseca AJM, Pereira JLS. Implications of seasonal and daily variation on methane and ammonia emissions from naturally ventilated dairy cattle barns in a Mediterranean climate: A two-year study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:173734. [PMID: 38857805 DOI: 10.1016/j.scitotenv.2024.173734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/12/2024]
Abstract
Seasonal and daily variations of gaseous emissions from naturally ventilated dairy cattle barns are important figures for the establishment of effective and specific mitigation plans. The present study aimed to measure methane (CH4) and ammonia (NH3) emissions in three naturally ventilated dairy cattle barns covering the four seasons for two consecutive years. In each barn, air samples from five indoor locations were drawn by a multipoint sampler to a photoacoustic infrared multigas monitor, along with temperature and relative humidity. Milk production data were also recorded. Results showed seasonal differences for CH4 and NH3 emissions in the three barns with no clear trends within years. Globally, diel CH4 emissions increased in the daytime with high intra-hour variability. The average hourly CH4 emissions (g h-1 livestock unit-1 (LU)) varied from 8.1 to 11.2 and 6.2 to 20.3 in the dairy barn 1, from 10.1 to 31.4 and 10.9 to 22.8 in the dairy barn 2, and from 1.5 to 8.2 and 13.1 to 22.1 in the dairy barn 3, respectively, in years 1 and 2. Diel NH3 emissions highly varied within hours and increased in the daytime. The average hourly NH3 emissions (g h-1 LU-1) varied from 0.78 to 1.56 and 0.50 to 1.38 in the dairy barn 1, from 1.04 to 3.40 and 0.93 to 1.98 in the dairy barn 2, and from 0.66 to 1.32 and 1.67 to 1.73 in the dairy barn 3, respectively, in years 1 and 2. Moreover, the emission factors of CH4 and NH3 were 309.5 and 30.6 (g day-1 LU-1), respectively, for naturally ventilated dairy cattle barns. Overall, this study provided a detailed characterization of seasonal and daily gaseous emissions variations highlighting the need for future longitudinal emission studies and identifying an opportunity to better adequate the existing mitigation strategies according to season and daytime.
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Affiliation(s)
- Ana R F Rodrigues
- REQUIMTE, LAQV, ICBAS, School of Medicine and Biomedical Sciences, University of Porto, R. de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.
| | - Maria Eduarda Silva
- University of Porto, School of Economics and Management, LIADD-INESC TEC, R. Dr. Roberto Frias, s/n, 4200-464 Porto, Portugal
| | - Vanessa F Silva
- University of Porto, Faculty of Sciences, CRACS-INESC TEC, R. Campo Alegre, s/n, 4169-007 Porto, Portugal
| | - Margarida R G Maia
- REQUIMTE, LAQV, ICBAS, School of Medicine and Biomedical Sciences, University of Porto, R. de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Ana R J Cabrita
- REQUIMTE, LAQV, ICBAS, School of Medicine and Biomedical Sciences, University of Porto, R. de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Henrique Trindade
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Inov4Agro, University of Trás-os-Montes and Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
| | - António J M Fonseca
- REQUIMTE, LAQV, ICBAS, School of Medicine and Biomedical Sciences, University of Porto, R. de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - José L S Pereira
- Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Inov4Agro, University of Trás-os-Montes and Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; Agrarian Higher School of Viseu, Polytechnic Institute of Viseu, Quinta da Alagoa, 3500-606 Viseu, Portugal; CERNAS-IPV Research Centre, Polytechnic Institute of Viseu, Campus Politécnico, Repeses, 3504-510 Viseu, Portugal
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Fresco S, Boichard D, Fritz S, Martin P. Genetic parameters for methane production, intensity, and yield predicted from milk mid-infrared spectra throughout lactation in Holstein dairy cows. J Dairy Sci 2024:S0022-0302(24)01192-5. [PMID: 39369894 DOI: 10.3168/jds.2024-25231] [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: 05/29/2024] [Accepted: 09/02/2024] [Indexed: 10/08/2024]
Abstract
Genetic selection to reduce methane (CH4) emissions is a promising solution for reducing the environmental impact of dairy cattle production. Before such a selection program can be implemented, however, it is necessary to have a better understanding of the genetic determinism of CH4 emissions and how this might influence other traits of interest. In this study, we performed a genetic analysis of 6 CH4 traits predicted from milk mid-infrared spectra. We predicted 4 CH4 traits in g/d (MeP, calculated using different prediction equations), one in g/kg of fat- and protein-corrected milk (MeI), and one in g/kg of dry matter intake (MeY). Using an external data set, we determined these prediction equations to be applicable in the range of 70 to 200 DIM. We then estimated genetic parameters in this DIM range using random regression models on a large data set of 829,025 spectra collected between January 2013 and February 2023 from 167,514 first- and second-parity Holstein cows. The 6 CH4 traits were found to be genetically stable throughout and across lactations, with average genetic correlations within a lactation ranging from 0.93 to 0.98, and those between lactations ranging from 0.92 to 0.98. All 6 CH4 traits were also found to be heritable, with average heritability ranging from 0.24 to 0.45. The average pairwise genetic correlations between the 6 CH4 traits ranged from -0.15 to 0.77, revealing that they are genetically distinct, including the 4 measurements of MeP. Of the 6 traits, 2 measures of MeP and MeI did not present antagonistic genetic correlations with milk yield, fat and protein contents, and SCS, and can probably be included in breeding goals with limited impact on other traits of interest.
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Affiliation(s)
- S Fresco
- Eliance, 149 rue de Bercy, 75595 Paris cedex 12, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France.
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - S Fritz
- Eliance, 149 rue de Bercy, 75595 Paris cedex 12, 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
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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.
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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
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Ma W, Ji X, Ding L, Yang SX, Guo K, Li Q. Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:4423. [PMID: 39001201 PMCID: PMC11244603 DOI: 10.3390/s24134423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024]
Abstract
The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11-17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.
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Affiliation(s)
- Weihong Ma
- College of Animal Science and Technology, Beijing University of Agriculture, Beijing 100096, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Xintong Ji
- College of Animal Science and Technology, Beijing University of Agriculture, Beijing 100096, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Luyu Ding
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
| | - Simon X Yang
- Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Kaijun Guo
- College of Animal Science and Technology, Beijing University of Agriculture, Beijing 100096, China
| | - Qifeng Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China
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Dressler EA, Bormann JM, Weaber RL, Rolf MM. Use of methane production data for genetic prediction in beef cattle: A review. Transl Anim Sci 2024; 8:txae014. [PMID: 38371425 PMCID: PMC10872685 DOI: 10.1093/tas/txae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
Methane (CH4) is a greenhouse gas that is produced and emitted from ruminant animals through enteric fermentation. Methane production from cattle has an environmental impact and is an energetic inefficiency. In the beef industry, CH4 production from enteric fermentation impacts all three pillars of sustainability: environmental, social, and economic. A variety of factors influence the quantity of CH4 produced during enteric fermentation, including characteristics of the rumen and feed composition. There are several methodologies available to either quantify or estimate CH4 production from cattle, all with distinct advantages and disadvantages. Methodologies include respiration calorimetry, the sulfur-hexafluoride tracer technique, infrared spectroscopy, prediction models, and the GreenFeed system. Published studies assess the accuracy of the various methodologies and compare estimates from different methods. There are advantages and disadvantages of each technology as they relate to the use of these phenotypes in genetic evaluation systems. Heritability and variance components of CH4 production have been estimated using the different CH4 quantification methods. Agreement in both the amounts of CH4 emitted and heritability estimates of CH4 emissions between various measurement methodologies varies in the literature. Using greenhouse gas traits in selection indices along with relevant output traits could provide producers with a tool to make selection decisions on environmental sustainability while also considering productivity. The objective of this review was to discuss factors that influence CH4 production, methods to quantify CH4 production for genetic evaluation, and genetic parameters of CH4 production in beef cattle.
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Affiliation(s)
- Elizabeth A Dressler
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
| | - Jennifer M Bormann
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
| | - Robert L Weaber
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
| | - Megan M Rolf
- Kansas State University, Department of Animal Sciences and Industry, Manhattan, KS 66506, USA
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Ghassemi Nejad J, Ju MS, Jo JH, Oh KH, Lee YS, Lee SD, Kim EJ, Roh S, Lee HG. Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies. Animals (Basel) 2024; 14:435. [PMID: 38338080 PMCID: PMC10854801 DOI: 10.3390/ani14030435] [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: 12/06/2023] [Revised: 01/16/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
This review examines the significant role of methane emissions in the livestock industry, with a focus on cattle and their substantial impact on climate change. It highlights the importance of accurate measurement and management techniques for methane, a potent greenhouse gas accounting for 14-16% of global emissions. The study evaluates both conventional and AI-driven methods for detecting methane emissions from livestock, particularly emphasizing cattle contributions, and the need for region-specific formulas. Sections cover livestock methane emissions, the potential of AI technology, data collection issues, methane's significance in carbon credit schemes, and current research and innovation. The review emphasizes the critical role of accurate measurement and estimation methods for effective climate change mitigation and reducing methane emissions from livestock operations. Overall, it provides a comprehensive overview of methane emissions in the livestock industry by synthesizing existing research and literature, aiming to improve knowledge and methods for mitigating climate change. Livestock-generated methane, especially from cattle, is highlighted as a crucial factor in climate change, and the review underscores the importance of integrating precise measurement and estimation techniques for effective mitigation.
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Affiliation(s)
- Jalil Ghassemi Nejad
- Department of Animal Science and Technology, Sanghuh College of Life Sciences, Konkuk University, Seoul 05029, Republic of Korea; (J.G.N.); (M.-S.J.); (J.-H.J.); (K.-H.O.)
| | - Mun-Su Ju
- Department of Animal Science and Technology, Sanghuh College of Life Sciences, Konkuk University, Seoul 05029, Republic of Korea; (J.G.N.); (M.-S.J.); (J.-H.J.); (K.-H.O.)
| | - Jang-Hoon Jo
- Department of Animal Science and Technology, Sanghuh College of Life Sciences, Konkuk University, Seoul 05029, Republic of Korea; (J.G.N.); (M.-S.J.); (J.-H.J.); (K.-H.O.)
| | - Kyung-Hwan Oh
- Department of Animal Science and Technology, Sanghuh College of Life Sciences, Konkuk University, Seoul 05029, Republic of Korea; (J.G.N.); (M.-S.J.); (J.-H.J.); (K.-H.O.)
| | - Yoon-Seok Lee
- School of Biotechnology, Hankyong National University, Anseong 17579, Republic of Korea;
- Center for Genetic Information, Hankyong National University, Anseong 17579, Republic of Korea
| | - Sung-Dae Lee
- Animal Nutrition and Physiology Division, National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea;
| | - Eun-Joong Kim
- Department of Animal Science, Kyungpook National University, Sangju 37224, Republic of Korea;
| | - Sanggun Roh
- Graduate School of Agricultural Science, Tohoku University, Sendai 980-8572, Japan;
| | - Hong-Gu Lee
- Department of Animal Science and Technology, Sanghuh College of Life Sciences, Konkuk University, Seoul 05029, Republic of Korea; (J.G.N.); (M.-S.J.); (J.-H.J.); (K.-H.O.)
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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.
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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
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