Applying assisted reproductive technology and reproductive management to reduce CO₂-equivalent emission in dairy and beef cattle: a review

Abstract

Methane emission from beef and dairy cattle combined contributes around 4.5-5.0% of total anthropogenic global methane. In addition to enteric methane (CH₄) produced by the rumen, cattle production also contributes carbon dioxide (CO₂) (feed), nitrous oxide (N₂O) (feed production, manure) and other CH₄ (manure) to the total greenhouse gas (GHG) budget of beef and dairy production systems. The relative contribution in standard dairy systems is typically enteric CH₄ 58%, feed 29% and manure 10%. Herds with low production efficiency can have an enteric CH₄ contribution up to 90%. Digestibility of feed can impact CH₄ emission intensity. Low fertility herds also have a greater enteric CH₄ contribution. Animals with good feed conversion efficiency have a lower emission intensity of CH₄/kg of meat or milk. Feed efficient heifers tend to be lean and have delayed puberty. Fertility is a major driver of profit in both beef and dairy cattle, and it is highly important to apply multi-trait selection when shifting herds towards improved efficiency and reduced CH₄. Single nucleotide polymorphisms (SNPs) have been identified for feed efficiency in cattle and are used in genomic selection. SNPs can be utilized in artificial insemination and embryo transfer to increase the proportion of cattle that have the attributes of efficiency, fertility and reduced enteric CH₄. Prepubertal heifers genomically selected for favourable traits can have oocytes recovered to produce IVF embryos. Reproductive technology is predicted to be increasingly adopted to reduce generation interval and accelerate the rate of genetic gain for efficiency, fertility and low CH₄ in cattle. The relatively high contribution of cattle to anthropogenic global methane has focussed attention on strategies to reduce enteric CH₄ without compromising efficiency and fertility. Assisted reproductive technology has an important role in achieving the goal of multiplying and distributing cattle that have good efficiency, fertility and low CH₄.

Keywords:
cattle; enteric methane; efficiency; fertility; assisted reproductive technology

Introduction

The global population of beef and dairy cattle combined is approximately 1.5 billion. Amongst domestic herbivores globally, cattle contribute about 20% of meat and 85% of milk. The global demand for meat and milk is projected to increase by 57% and 48%, respectively, between 2005 and 2050 (Alexandratos and Bruinsma, 2012). Cattle, therefore, will continue to have a very important role in future global food security (Davis and White, 2020). Beef and dairy production occur across diverse environments and in both extensive and intensive systems (Faverdin et al., 2022). Extensive cattle grazing is found in rangelands and savannas that are suited to low-input and low-cost animal production. Intensive beef and dairy systems utilize, and add value to, feed sources that are either unsuitable or surplus to human consumption. Grazing lands cover about 25% of the global landmass (Mottet et al., 2018) and intensive beef accounts for <15% of global beef production (Mottet et al., 2017). The environmental footprint of cattle production has received increased attention globally (Knapp et al., 2014; Faverdin et al., 2022). Methane emission from cattle has been recognised for around 30 years (Johnson and Johnson, 1995) and has become a particular focus as cattle contribute around 4.5-5.0% of total anthropogenic global methane (Wallace et al., 2015; Hayes et al., 2016; de Haas et al., 2021; Faverdin et al., 2022; Hossein-Zadeh, 2022; Galyean and Hales, 2023). Most methane produced by cattle is from enteric fermentation of complex carbohydrates into simple sugars by methanogenic protozoa (Bowen et al., 2020). The biology and function of the rumen has been well reviewed (Ross et al., 2013; Knapp et al., 2014). The ability to digest cellulolytic material into usable energy and protein is arguably the greatest advantage but also the greatest disadvantage of cattle. The relative abundance of ruminal methanogenic and non-methanogenic microbes influence the amount of methane produced by an individual animal (Bowen et al., 2020). The population of ruminal microbes can now be determined by microbial gene abundance (Roehe et al., 2016).

Assisted reproductive technologies can have a major impact on improving productivity in beef and dairy cattle. Artificial insemination (AI) and multiple ovulation and embryo transfer (MOET) increase the rate of dissemination of animals with traits that have high genetic merit and high productive capacity. However, the mature technologies of AI and MOET do not increase the rate of genetic gain from one generation to the next. The latter is controlled by generation interval which is relatively long in cattle (Schefers and Weigel, 2012; Kasinathan et al. 2015). Generation interval can be shortened in cattle by utilising oocytes from heifers early in life to produce IVF embryos (Baruselli et al., 2016; Baldassarre and Bordignon, 2018). This review seeks to demonstrate how assisted reproductive technology (ART) and reproductive management can be used to generate cattle that have improved efficiency and produce less methane.

Reproductive efficiency in cattle and application of artificial insemination to improve efficiency and reduce methane emission

In beef cattle, the cow-calf unit utilizes approximately 70% of resources. Selection for reproductive efficiency therefore has a major bearing on both efficiency and profitability. With high reproductive efficiency, fewer cows are required to produce the next generation of calves, and this reduces resource requirement, herd methane production, and costs (Hegarty and McEwan, 2010). Also, reproductively inefficient cows are removed from herds. In a United States beef production system, an improvement in reproductive efficiency (0.5 to 1 calves/year) resulted in a 34% reduction in water use, 44% reduction in land use, and 39% reduction in the CO₂-equivalent (CO₂-eq) footprint (Davis and White, 2020). ART can be incorporated into beef breeding programs to further improve efficiency and reduce CO₂-eq emission intensity. In Brazil, the use of timed artificial insemination (TAI) in a breeding herd reduced age at first calving from 48 to 24 months and increased weaning rate from 60% to 80% compared with natural mating (Abreu et al., 2022). There was a 37.7% reduction in pasture required and 85.4% reduction in CO₂-eq to produce 400 calves (Abreu et al., 2022). The CO₂-eq was calculated according to livestock units (1 LU=450 kg of live weight) and a stocking rate of 1 LU per hectare of pasture was estimated to produce calves (Figueiredo et al., 2017). The low reproductive efficiency system (natural mating) emitted 3,714.5 tons of CO₂-eq per year while the high reproductive efficiency system (TAI) emitted 2,311.3 tons of CO₂-eq annually. The TAI system generated US$84,196 in credit for reducing CO₂-eq emissions (quoted at US$60 per 1-ton CO₂-eq). TAI has been applied in beef heifers to reduce age at first pregnancy and calving (Baruselli et al., 2017) which impacts lifetime reproductive efficiency and CO₂-eq emissions. TAI can also be utilized to manage inter-calving intervals so that cows produce a calf annually (Sá et al., 2013; Baruselli et al., 2018a).

The same basic principles addressed above apply in dairy cattle (Hutchinson et al., 2013). For example, lowering the age at first calving and culling frequency reduced the number of replacement heifers needed and enteric methane emission per unit of kg energy-corrected milk (CH₄/ECM; Knapp et al., 2014). Improving the fertility of dairy herds can potentially reduce methane emission by up to 25% (Garnworthy, 2004). We recently studied the influence of calving interval (CI, i.e. reproductive efficiency) on the CO₂-eq footprint of lactating dairy cows using life cycle assessment methodology (Abreu et al., 2023). A comparison was made between production and CO₂-eq/milk (corrected for fat and protein content) of cows with a CI of 13 or 15 months. The lactation period was estimated at 11 and 13 months for cows with a CI of 13 or 15 months, respectively (Cole and Null, 2009; Biassus et al., 2010). Total greenhouse gas emissions for 1 kg of milk (CO₂-eq/milk) was 0.657 when the CI index was 13 months and 0.703 (7% increase) when the CI index was 15 months.

Embryo technology to mitigate methane emission

Dairy cattle can suffer heat stress (HS) during summer which decreases dry matter intake (DMI), daily gain, milk yield, and fertility (Kadzere et al., 2002; Hansen, 2007). During HS, milk production decreases more than dry matter intake which increases the CO₂-eq emission/kg energy-corrected milk (Rhoads et al., 2009). HS contributes to culling and death of cows (St-Pierre et al., 2003). The reduction in fertility is associated with altered ovarian folliculogenesis and oviductal function and increased embryonic mortality. The latter can be managed during periods of HS by replacing natural mating and artificial insemination (AI) with the transfer of either in vivo or in vitro derived embryos to cows on day 7 of the estrous cycle (Hansen, 2007; Baruselli et al., 2020).

We developed a simulation model which compared the use of AI or embryo transfer (ET) in HS dairy cows (Figure 1). The model assumed that pregnancy per AI (P/AI) and P/ET during HS were around 17 and 40%, respectively, and the service rate was 60% for AI and 50% for ET (ET was performed only on animals with a corpus luteum) (Baruselli et al., 2018b). The pregnancy rate following 105 days of breeding was 34.6% for AI and 53.1% for ET (53.6% increase). Cows subjected to AI had a greater number of days open (59.3 days) than cows exposed to ET (52.5 days) after the beginning of the breeding program. This shows that it is possible to increase the 21-day pregnancy rate by eight percentage points using ET in place of AI in HS dairy cows. As noted earlier, shorter inter-calving intervals are associated with a reduced CO₂-eq budget in cattle.

Figure 1
Survival curve assuming 60% service rate, 17% conception rate (P/AI) and 10% pregnancy rate every 21 days in repeat breeders and heat stressed dairy cows during 105-day AI program (pregnancy loss of 19% between 30 and 60 days gestation). For ET program, it was assumed 50% service rate (ET only in recipients with a corpus luteum), 40% conception rate (P/ET) and 15.3% pregnancy rate every 21 days in heat stressed dairy cows during a 105-day ET program (pregnancy loss of 21% between 30 and 60 days gestation). Adapted from Baruselli et al. (2018b).

As noted earlier in this review, the mature technologies of AI and MOET do not increase the rate of genetic gain. The latter is controlled by generation interval which is relatively long in cattle (Schefers and Weigel, 2012; Kasinathan et al., 2015). Generation interval can be shortened in cattle by utilizing oocytes from heifers early in life. Waves of follicular growth occur before birth and in the first weeks after birth in heifers (Evans et al., 1994a, 1994b; Monteiro et al., 2009). Oocytes can be recovered before birth (velogenesis; Betteridge et al., 1989; Georges and Massey, 1991; Kauffold et al., 2005) and well before puberty (Onuma et al., 1970; Baruselli et al., 2016), and used to generate viable embryos in the laboratory using in vitro embryo production (IVEP) (Baruselli et al., 2021). Prepubertal heifers show a good ovarian follicular response to FSH superstimulation and a relatively large number of oocytes can be retrieved for IVEP (Baruselli et al., 2021). IVEP is less efficient for oocytes from young heifers compared with mature heifers and cows and further research is needed to optimize IVEP in prepubertal heifers (Baruselli et al., 2021). Notwithstanding, IVEP with oocytes from young heifers has emerged as a fundamental enabling technology for the exploitation of genomic selection to produce cattle defined by efficiency, fertility and low CH₄ emission.

Balancing feed efficiency in meat and milk production with fertility and low CO₂-eq emission

Cattle consume a relatively large amount of biomass and have a low feed conversion ratio compared with other livestock (FAO, 2018; Mottet et al., 2018). The provision of feed typically accounts for 70-80% of production costs in both extensive and intensive systems (Mottet et al., 2018). There is considerable interest, therefore, in identifying and multiplying cattle that have improved feed efficiency (Løvendahl et al., 2018; Davis and White, 2020). This applies to both extensive and intensive systems (Hietala and Juga, 2017; Kava et al., 2023). Associations between feed efficiency, methane production, and sustainability, have been known for more than 20 years (Arthur and Herd, 2005; Nkrumah et al., 2006; Freetly and Brown-Brandl, 2013). The relatively high heritability of growth and feed efficiency in cattle was recognised some 70 years ago and subsequently confirmed (Knapp and Nordskog, 1946; Berry and Crowley, 2013; Gonzalez-Recio et al., 2014; Sypniewski et al., 2021).

More recently, single nucleotide polymorphisms (SNPs) have been identified for feed efficiency in cattle and have been used in genomic selection (Arthur, 2015; Seabury et al., 2017; Sypniewski et al., 2021; Madilindi et al., 2022; Buss et al., 2023). As noted earlier, the relative abundance of ruminal acetogenic and methanogenic microbes influences methane emission by individual animals. There is a significant host effect on the ruminal microbe population, and it has been proposed that microbial gene abundance can be used to select cattle for feed efficiency and growth (Roehe et al., 2016). The genome of cattle can influence the population of ruminal microbes and hence the ruminal microbe genome profile which determines methane production (Difford et al., 2018; O’Hara et al., 2020; Gonzalez-Recio et al., 2023). Characterization of the ruminal microbe gene profile has been proposed as an alternative to expensive, time consuming methods for measuring feed efficiency in individual cattle (Arthur and Herd, 2005; Basarab et al., 2013; Kenny et al., 2018; Terry et al., 2021).

Growth and feed efficiency genes show single nucleotide polymorphism (Abo-Ismail et al., 2013; Seabury et al., 2017; Madilindi et al., 2022; Buss et al., 2023). Methane emission also shows single nucleotide polymorphism in cattle (Sarghale et al., 2020). The advent of molecular gene markers has created the opportunity to accurately identify cattle with desirable genes and to then use ART to rapidly multiply and disseminate cattle with improved feed efficiency and growth performance. Efficient cattle were reported in one study to have reduced CH₄(g/day) and CO₂-equivalent (g/day) emissions (Callegaro et al., 2022). The breeding technology used to generate efficient cattle will be governed by the production system and resources available. For example, AI and ET are already utilized in intensive dairy systems. Artificial insemination can be adopted in extensive beef systems as demonstrated in Latin America (Baruselli et al., 2004; Ferraz et al., 2012; Sartori et al., 2016; Mapletoft et al., 2018; Bó et al., 2018). Low-input, low-cost beef systems (North and South America, northern Australia, South Asia, Sub-Saharan Africa) will continue to rely on natural mating. For these regions, central breeding facilities will utilize genomic selection and ART to produce male embryos and/or bulls for dissemination for natural mating.

Whilst feed efficiency is undoubtedly a commercially important trait in beef and dairy cattle, selection for feed efficiency should not be at the expense of other important traits (Mu et al., 2016). As this review has argued, fertility has a major impact on enterprise productivity and profit in both beef and dairy systems. Studies in young growing British and European (Bos taurus) bulls consistently showed negative associations between feed efficiency and fertility measures including testicular growth and morphology and the characteristics of seminal plasma and spermatozoa (Awda et al., 2013; Fontoura et al., 2016; Montanholi et al., 2016; Bourgon et al., 2018). In contrast, a study in growing composite bulls (Bos taurus x Bos indicus) found that fertility measures did not differ for bulls of different feed efficiency (Kowalski et al., 2017).

Heifers with improved feed efficiency were reported to be leaner and reached puberty later than heifers with lesser feed efficiency (Randel and Welsh, 2013). In another study, heifers with good feed efficiency attained puberty earlier than heifers with poorer feed efficiency (Canal et al., 2020). Other studies in female cattle have also shown either a negative effect of feed efficiency on fertility (Mu et al., 2016; Ferreira et al., 2018) or no effect (Crowley et al., 2011; Davis et al., 2016). A study in dairy cows under commercial conditions reported that cows with high feed efficiency had a greater inter-calving interval (Vallimont et al., 2013). Dairy cattle selected for milk yield and feed efficiency had a reduced methane budget resulting from increased milk yield (Knapp et al. 2014). The impact of this selection strategy in an intensive dairy system was estimated to be a reduction of 9-19% in CO₂-eq emission/kg energy-corrected milk (Knapp et al. 2014). In another study in dairy cows, selection based on genetic potential for milk production was associated with a decline in fertility, an increase in non-productive cows, and overall increase in CO₂-eq emission for the production system (O’Brien et al., 2010). Another study in dairy cows reported low genetic correlations between methane production and fertility traits (Zetouni et al., 2018). Given the contrasting reports there is a need for further studies on feed efficiency, methane production, and lifetime fertility in cattle. The above studies have also demonstrated the importance of multi-trait selection in cattle breeding programs and the need to balance feed efficiency with other commercially important traits, in particular fertility (Bonamy et al., 2019).

Enteric methane in production system life cycle assessment

Enteric methane forms part of the broader greenhouse gas (GHG) budget of beef and dairy production systems (Ibidhi and Calsamiglia, 2020). The broader GHG budget includes methane, nitrous oxide (N₂O) and CO₂ emission from manure, feed production, vehicles and transport, and other plant and equipment. The total GHG budget of a production system is determined by life cycle assessment (LCA) methodology standardized by ISO 14040 (ISO, 2006a) and ISO 14044 (ISO, 2006b) (de Vries et al., 2015; Kyttä et al., 2022). The relative contribution of different components of production systems to the GHG budget can vary greatly for different beef and dairy systems. One estimate for milk production was enteric methane 58.5% (CH₄), feed production 29.4% (CO₂, N₂O) and manure 9.5% (CH₄, N₂O; FAO, 2018). The relative contribution of enteric methane can reach 91% in low efficiency systems (Chhabra et al., 2013). The digestibility of feed can also have a major impact on enteric methane contribution to the overall GHG budget (Pinares-Patiño et al., 2007; FAO, 2019; Eugéne et al., 2021; Congio et al., 2022). Herds with high fertility and high production efficiency have a reduced GHG budget (Strandén et al., 2022). In low fertility herds, replacement heifers can contribute up to 27% to the GHG budget (Garnworthy, 2004). The contribution of replacement heifers decreases to 10-12% in high fertility herds. High fertility herds with fewer replacement heifers require less feed production and have reduced manure, which lowers methane and nitrous oxide emission.

Conclusions and future direction

The global attention on enteric CH₄ production in cattle requires a response that involves collaboration between researchers and industry. Future generations of cattle will be characterized by better efficiency and fertility, which may reduce CH₄ emission intensity. This will result from balanced multi-trait selection. There has been progress in the discovery of SNPs for efficiency and methane emission in cattle. These SNPs will be incorporated into assisted reproductive technology such as AI and ET for targeted multiplication and dispersal of cattle with defined production and environmental credentials. The urgency in moving to the next generation of cattle will see an increase in the production of embryos from genomically defined prepubertal heifers. This will reduce generation interval and accelerate the rate of genetic improvement to cattle defined by better efficiency and fertility and lower CH₄ emission. The opportunity for cattle to be a part of ecosystem management was recently highlighted (Thompson et al., 2023). The challenge remains to communicate the importance of cattle for food security and the environment (Manzano et al., 2023).

• Financial support: FAPESP (15/19563-0 and 19/14679-1), CNPq (306759/2016-0 and 315978/2021-0) and CAPES (Grant number 001).
• How to cite: Baruselli PS, Abreu LÂ, Paula VR, Carvalho B, Gricio EA, Mori FK, Rebeis LM, Albertini S, Souza AH, D’Occhio M. Applying assisted reproductive technology and reproductive management to reduce CO₂-equivalent emission in dairy and beef cattle: a review. Anim Reprod. 2023;20(2):e20230060. https://doi.org/10.1590/1984-3143-AR2023-0060

References

• Abo-Ismail MK, Kelly MJ, Squires EJ, Swanson KC, Bauck S, Miller SP. Identification of single nucleotide polymorphisms in genes involved in digestive and metabolic processes associated with feed efficiency and performance traits in beef cattle. J Anim Sci. 2013;91(6):2512-29. http://dx.doi.org/10.2527/jas.2012-5756 PMid:23508024.
  » http://dx.doi.org/10.2527/jas.2012-5756
• Abreu LA, Paula VR, Carvalho BC, Souza AH, Rebeis LM, Mori FK, Gricio E, Baruselli PS. Influence of calving interval on the carbon footprint of lactating dairy cows under the life cycle assessment metric. Animal Science Proceedings. 2023;14(3):529-30. http://dx.doi.org/10.1016/j.anscip.2023.03.159
  » http://dx.doi.org/10.1016/j.anscip.2023.03.159
• Abreu LA, Rezende VT, Gameiro AH, Baruselli PS. Effect of reduced age at first calving and an increased weaning rate on CO₂ equivalent emissions in a cow-calf system. Revista Engenharia na Agricultura. 2022;30:311-8. http://dx.doi.org/10.13083/reveng.v30i1.14028
  » http://dx.doi.org/10.13083/reveng.v30i1.14028
• 12-03 Alexandratos N, Bruinsma J. World Agriculture Towards 2030/2050: The 2012 Revision. Rome: FAO; 2012. (ESA Working Paper No12-03
• Arthur PF, Herd RM. Efficiency of feed utilisation by livestock - Implications and benefits of genetic improvement. Can J Anim Sci. 2005;85(3):281-90. http://dx.doi.org/10.4141/A04-062
  » http://dx.doi.org/10.4141/A04-062
• Arthur PF. Genetic technologies to reduce methane emissions from Australian beef cattle: research Project, Final Report. 2015 [cited 2023 May 16]. Available from: https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0006/584178/genetic-technologies-to-reduce-methane-emissions-from-australian-beef-cattle.pdf
  » https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0006/584178/genetic-technologies-to-reduce-methane-emissions-from-australian-beef-cattle.pdf
• Awda BJ, Miller SP, Montanholi YR, Vander Voort G, Caldwell T, Buhr MM, Swanson KC. The relationship between feed efficiency traits and fertility in young beef bulls. Can J Anim Sci. 2013;93(2):185-92. http://dx.doi.org/10.4141/cjas2012-092
  » http://dx.doi.org/10.4141/cjas2012-092
• Baldassarre H, Bordignon V. Laparoscopic ovum pick-up for in vitro embryo production from dairy bovine and buffalo calves. Anim Reprod. 2018;15(3):191-6. http://dx.doi.org/10.21451/1984-3143-AR2018-0057 PMid:34178141.
  » http://dx.doi.org/10.21451/1984-3143-AR2018-0057
• Baruselli PS, Batista EOS, Vieira LM, Ferreira RM, Guerreiro BG, Bayeux BM, Sales JNS, Souza AH, Gimenes LU. Factors that interfere with oocyte quality for in vitro production of cattle embryos: effects of different developmental & reproductive stages. Anim Reprod. 2016;13(3):264-72. http://dx.doi.org/10.21451/1984-3143-AR861
  » http://dx.doi.org/10.21451/1984-3143-AR861
• Baruselli PS, Ferreira RM, Colli MHA, Elliff FM, Sá MF Fo, Vieira LM, Freitas BG. Timed artificial insemination: current challenges and recent advances in reproductive efficiency in beef and dairy herds in Brazil. Anim Reprod. 2017;14(3):558-71. http://dx.doi.org/10.21451/1984-3143-AR999
  » http://dx.doi.org/10.21451/1984-3143-AR999
• Baruselli PS, Ferreira RM, Vieira LM, Souza AH, Bó GA, Rodrigues CA. Use of embryo transfer to alleviate infertility caused by heat stress. Theriogenology. 2020;155:1-11. http://dx.doi.org/10.1016/j.theriogenology.2020.04.028 PMid:32562738.
  » http://dx.doi.org/10.1016/j.theriogenology.2020.04.028
• Baruselli PS, Reis EL, Marques MO, Nasser LF, Bó GA. The use of hormonal treatments to improve reproductive performance of anestrous beef cattle in tropical climates. Anim Reprod Sci. 2004;82-83:479-86. http://dx.doi.org/10.1016/j.anireprosci.2004.04.025 PMid:15271474.
  » http://dx.doi.org/10.1016/j.anireprosci.2004.04.025
• Baruselli PS, Rodrigues CA, Ferreira RM, Sales JNS, Elliff FM, Silva LG, Viziack MP, Factor L, D’Occhio MJ. Impact of oocyte donor age and breed on in vitro embryo production in cattle, and relationship of dairy and beef embryo recipients on pregnancy and the subsequent performance of offspring: A review. Reprod Fertil Dev. 2021;34(2):36-51. http://dx.doi.org/10.1071/RD21285 PMid:35231233.
  » http://dx.doi.org/10.1071/RD21285
• Baruselli PS, Ferreira RM, Sá MF Fo, Bó GA. Review: using artificial insemination vs. natural service in beef herds. Animal. 2018a;12(S1):s45-52. http://dx.doi.org/10.1017/S175173111800054X PMid:29554986.
  » http://dx.doi.org/10.1017/S175173111800054X
• Baruselli PS, Souza AH, Sá MF Fo, Marques MO, Sales JNS. Genetic market in cattle (Bull, AI, FTAI, MOET and IVP): financial payback based on reproductive efficiency in beef and dairy herds in Brazil. Anim Reprod. 2018b;15(3):247-55. http://dx.doi.org/10.21451/1984-3143-AR2018-0091 PMid:34178148.
  » http://dx.doi.org/10.21451/1984-3143-AR2018-0091
• Basarab JA, Beauchemin KA, Baron VS, Ominski KH, Guan LL, Miller SP, Crowley JJ. Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production. Animal. 2013;7(Suppl 2):303-15. http://dx.doi.org/10.1017/S1751731113000888 PMid:23739472.
  » http://dx.doi.org/10.1017/S1751731113000888
• Berry DP, Crowley JJ. Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle. J Anim Sci. 2013;91(4):1594-613. http://dx.doi.org/10.2527/jas.2012-5862 PMid:23345557.
  » http://dx.doi.org/10.2527/jas.2012-5862
• Betteridge KJ, Smith C, Stubbings RB, Xu KP, King WA. Potential genetic improvement of cattle by fertilization of fetal oocytes in vitro. J Reprod Fertil Suppl. 1989;38:87-98. PMid:2677352.
• Biassus IO, Cobuci JA, Costa CN, Rorato PRN, Braccini Neto J, Cardoso LL. Persistence in milk, fat and protein production of primiparous Holstein cows by random regression models. Rev Bras Zootec. 2010;39(12):2617-24. http://dx.doi.org/10.1590/S1516-35982010001200009
  » http://dx.doi.org/10.1590/S1516-35982010001200009
• Bó GA, Huguenine E, de la Mata JJ, Núñez-Olivera R, Baruselli PS, Menchaca A. Programs for fixed-time artificial insemination in South American beef cattle. Anim Reprod. 2018;15(Suppl 1):952-62. http://dx.doi.org/10.21451/1984-3143-AR2018-0025 PMid:36249833.
  » http://dx.doi.org/10.21451/1984-3143-AR2018-0025
• Bonamy M, Kluska S, Peripolli E, Lemos MVA, Amorim ST, Vaca RJ, Lôbo RB, Castro LM, Faria CU, Ferrari FB, Baldi F. Genetic association between different criteria to define sexual precocious heifers with growth, carcass, reproductive and feed efficiency indicator traits in Nellore cattle using genomic information. J Anim Breed Genet. 2019;136(1):15-22. http://dx.doi.org/10.1111/jbg.12366 PMid:30461083.
  » http://dx.doi.org/10.1111/jbg.12366
• Bourgon SL, Diel de Amorim M, Chenier T, Sargolzaei M, Miller SP, Martell JE, Montanholi YR. Relationships of nutritional plane and feed efficiency with sexual development and fertility related measures in young beef bulls. Anim Reprod Sci. 2018;198:99-111. http://dx.doi.org/10.1016/j.anireprosci.2018.09.007 PMid:30219379.
  » http://dx.doi.org/10.1016/j.anireprosci.2018.09.007
• Bowen JM, Cormican P, Lister SJ, McCabe MS, Duthie CA, Roehe R, Dewhurst RJ. Links between the rumen microbiota, methane emissions and feed efficiency of finishing steers offered dietary lipid and nitrate supplementation. PLoS One. 2020;15(4):e0231759. http://dx.doi.org/10.1371/journal.pone.0231759 PMid:32330150.
  » http://dx.doi.org/10.1371/journal.pone.0231759
• Buss CE, Afonso J, de Oliveira PSN, Petrini J, Tizioto PC, Cesar ASM, Gustani-Buss EC, Cardoso TF, Rovadoski GA, da Silva Diniz WJ, de Lima AO, Rocha MIP, Andrade BGN, Wolf JB, Coutinho LL, Mourão GB, de Almeida Regitano LC. Bivariate GWAS reveals pleiotropic regions among feed efficiency and beef quality-related traits in Nelore cattle. Mamm Genome. 2023;34(1):90-103. http://dx.doi.org/10.1007/s00335-022-09969-6 PMid:36463529.
  » http://dx.doi.org/10.1007/s00335-022-09969-6
• Callegaro S, Niero G, Penasa M, Finocchiaro R, Invernizzi G, Cassandro M. Greenhouse gas emissions, dry matter intake and feed efficiency of young Holstein bulls. Ital J Anim Sci. 2022;21(1):870-7. http://dx.doi.org/10.1080/1828051X.2022.2071178
  » http://dx.doi.org/10.1080/1828051X.2022.2071178
• Canal LB, Fontes PLP, Sanford CD, Mercadante VRG, DiLorenzo N, Lamb GC, Oosthuizen N. Relationships between feed efficiency and puberty in Bos taurus and Bos indicus-influenced replacement beef heifers. J Anim Sci. 2020;98(10):1-9. http://dx.doi.org/10.1093/jas/skaa319 PMid:32978943.
  » http://dx.doi.org/10.1093/jas/skaa319
• Chhabra A, Manjunath KR, Panigrahy S, Parihar JS. Greenhouse gas emissions from Indian livestock. Clim Change. 2013;117(1-2):329-44. http://dx.doi.org/10.1007/s10584-012-0556-8
  » http://dx.doi.org/10.1007/s10584-012-0556-8
• Cole JB, Null DJ. Genetic evaluation of lactation persistency for five breeds of dairy cattle. J Dairy Sci. 2009;92(5):2248-58. http://dx.doi.org/10.3168/jds.2008-1825 PMid:19389984.
  » http://dx.doi.org/10.3168/jds.2008-1825
• Congio GFS, Bannink A, Mayorga OL, Rodrigues JPP, Bougouin A, Kebreab E, Silva RR, Mauricio RM, da Silva SC, Oliveira PPA, Munoz C, Pereira LGR, Gomez C, Ariza-Nieto C, Ribeiro-Filho HMN, Castelan-Ortega OA, Rosero-Noguera JR, Tieri MP, Rodrigues PHM, Marcondes MI, Astigarraga L, Abarca S, Hristov AN. Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database. Sci Total Environ. 2022;825:153982. http://dx.doi.org/10.1016/j.scitotenv.2022.153982 PMid:35202679.
  » http://dx.doi.org/10.1016/j.scitotenv.2022.153982
• Crowley JJ, Evans RD, McHugh N, Kenny DA, McGee M, Crews DH Jr, Berry DP. Genetic relationships between feed efficiency in growing males and beef cow performance. J Anim Sci. 2011;89(11):3372-81. http://dx.doi.org/10.2527/jas.2011-3835 PMid:21680792.
  » http://dx.doi.org/10.2527/jas.2011-3835
• Davis ME, Lancaster PA, Rutledge JJ, Cundiff LV. Life cycle efficiency of beef production: VIII. Relationship between residual feed intake of heifers and subsequent cow efficiency ratios. J Anim Sci. 2016;94(11):4860-71. http://dx.doi.org/10.2527/jas.2016-0690 PMid:27898944.
  » http://dx.doi.org/10.2527/jas.2016-0690
• Davis TC, White RR. Breeding animals to feed people: the many roles of animal reproduction in ensuring global food security. Theriogenology. 2020;150:27-33. http://dx.doi.org/10.1016/j.theriogenology.2020.01.041 PMid:32088028.
  » http://dx.doi.org/10.1016/j.theriogenology.2020.01.041
• 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. http://dx.doi.org/10.1016/j.animal.2021.100294 PMid:34246599.
  » http://dx.doi.org/10.1016/j.animal.2021.100294
• Difford GF, Plichta DR, Løvendahl P, Lassen J, Noel SJ, Højberg O, Wright ADG, Zhu Z, Kristensen L, Nielsen HB, Guldbrandtsen B, Sahana G. Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLoS Genet. 2018;14(10):e1007580. http://dx.doi.org/10.1371/journal.pgen.1007580 PMid:30312316.
  » http://dx.doi.org/10.1371/journal.pgen.1007580
• Eugéne M, Klumpp K, Sauvant D. Methane mitigating options with forage fed ruminants. Grass Forage Sci. 2021;76(2):196-204. http://dx.doi.org/10.1111/gfs.12540
  » http://dx.doi.org/10.1111/gfs.12540
• Evans ACO, Adams GP, Rawling NC. Follicular and hormonal development in prepubertal heifers from 2 to 36 weeks of age. J Reprod Fertil. 1994a;102(2):463-70. http://dx.doi.org/10.1530/jrf.0.1020463 PMid:7861402.
  » http://dx.doi.org/10.1530/jrf.0.1020463
• Evans ACO, Adams GP, Rawlings NC. Endocrine and ovarian follicular changes leading up to the first ovulation in prepubertal heifers. J Reprod Fertil. 1994b;100(1):187-94. http://dx.doi.org/10.1530/jrf.0.1000187 PMid:8182588.
  » http://dx.doi.org/10.1530/jrf.0.1000187
• FAO. Climate Smart Agriculture Sourcebook, 2018 [cited 2023 May 16]. Available from: www.fao.org/climate-smart-agriculture-sourcebook/about/new-content/en/
  » www.fao.org/climate-smart-agriculture-sourcebook/about/new-content/en/
• FAO. Livestock and enteric methane. 2019 [cited 2023 May 16]. Available from: www.fao.org/in-action/enteric-methane/en/
  » www.fao.org/in-action/enteric-methane/en/
• Faverdin P, Guyomard H, Puillet L, Forslund A. Animal board invited review: Specialising and intensifying cattle production for better efficiency and less global warming: contrasting results for milk and meat co-production at different scales. Animal. 2022;16(1):100431. http://dx.doi.org/10.1016/j.animal.2021.100431 PMid:34996025.
  » http://dx.doi.org/10.1016/j.animal.2021.100431
• Ferraz JBS, Eler JP, Rezende FM. Impact of using artificial insemination on the multiplication of high genetic merit beef cattle in Brazil. Anim Reprod. 2012;9(3):133-8.
• Ferreira RJ Jr, Bonilha SFM, Monteiro FM, Cyrillo JNSG, Branco RH, Silva JAV, Mercadante MEZ. Evidence of negative relationship between female fertility and feed efficiency in Nellore cattle. J Anim Sci. 2018;96(10):4035-44. http://dx.doi.org/10.1093/jas/sky276 PMid:29986041.
  » http://dx.doi.org/10.1093/jas/sky276
• Figueiredo EB, Jayasundara S, Bordonal RO, Berchielli TT, Reis RA, Wagner-Riddle C, La Scala N Jr. Greenhouse gas balance and carbon footprint of beef cattle in three contrasting pasture-management systems in Brazil. J Clean Prod. 2017;142(1):420-31. http://dx.doi.org/10.1016/j.jclepro.2016.03.132
  » http://dx.doi.org/10.1016/j.jclepro.2016.03.132
• Fontoura ABP, Montanholi YR, Diel De Amorim M, Foster RA, Chenier T, Miller SP. Associations between feed efficiency, sexual maturity and fertility-related measures in young beef bulls. Animal. 2016;10(1):96-105. http://dx.doi.org/10.1017/S1751731115001925 PMid:26351012.
  » http://dx.doi.org/10.1017/S1751731115001925
• Freetly HC, Brown-Brandl TM. Enteric methane production from beef cattle that vary in feed efficiency. J Anim Sci. 2013;91(10):4826-31. http://dx.doi.org/10.2527/jas.2011-4781 PMid:23965389.
  » http://dx.doi.org/10.2527/jas.2011-4781
• Galyean ML, Hales KE. Feeding management strategies to mitigate methane and improve production efficiency in feedlot cattle. Animals (Basel). 2023;13(4):758. http://dx.doi.org/10.3390/ani13040758 PMid:36830545.
  » http://dx.doi.org/10.3390/ani13040758
• Garnworthy PC. The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Anim Feed Sci Technol. 2004;112(1-4):211-23. http://dx.doi.org/10.1016/j.anifeedsci.2003.10.011
  » http://dx.doi.org/10.1016/j.anifeedsci.2003.10.011
• Georges M, Massey JM. Velogenetics, or the synergistic use of marker assisted selection and germ-line manipulation. Theriogenology. 1991;35(1):151-9. http://dx.doi.org/10.1016/0093-691X(91)90154-6
  » http://dx.doi.org/10.1016/0093-691X(91)90154-6
• Gonzalez-Recio O, Pryce JE, Haile-Mariam M, Hayes BJ. Incorporating heifer feed efficiency in the Australian selection index using genomic selection. J Dairy Sci. 2014;97(6):3883-93. http://dx.doi.org/10.3168/jds.2013-7515 PMid:24679937.
  » http://dx.doi.org/10.3168/jds.2013-7515
• Gonzalez-Recio O, Scrobota N, López-Paredes J, Saborío-Montero A, Fernández A, López de Maturana E, Villanueva B, Goiri I, Atxaerandio R, García-Rodríguez A. Review: diving into the cow hologenome to reduce methane emissions and increase sustainability. Animal. 2023;17(2):100780. http://dx.doi.org/10.1016/j.animal.2023.100780
  » http://dx.doi.org/10.1016/j.animal.2023.100780
• Hansen PJ. Exploitation of genetic and physiological determinants of embryonic resistance to elevated temperature to improve embryonic survival in dairy cattle during heat stress. Theriogenology. 2007;68(Suppl 1):S242-9. http://dx.doi.org/10.1016/j.theriogenology.2007.04.008 PMid:17482669.
  » http://dx.doi.org/10.1016/j.theriogenology.2007.04.008
• Hayes BJ, Donoghue KA, Reich CM, Mason BA, Bird-Gardiner T, Herd RM, Arthur PF. Genomic heritabilities and genomic estimated breeding values for methane traits in Angus cattle. J Anim Sci. 2016;94(3):902-8. http://dx.doi.org/10.2527/jas.2015-0078 PMid:27065252.
  » http://dx.doi.org/10.2527/jas.2015-0078
• Hegarty RS, McEwan JC. Genetic opportunities to reduce enteric methane emissions from ruminant livestock. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production; Leipzig, Germany. Local: Publisher German Society for Animal Science; 2010. p. 515.
• Hietala P, Juga J. Impact of including growth, carcass and feed efficiency traits in the breeding goal for combined milk and beef production systems. Animal. 2017;11(4):564-73. http://dx.doi.org/10.1017/S1751731116001877 PMid:27608523.
  » http://dx.doi.org/10.1017/S1751731116001877
• Hossein-Zadeh NG. Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis. Sci Rep. 2022;12(1):12352. http://dx.doi.org/10.1038/s41598-022-16778-z PMid:35853993.
  » http://dx.doi.org/10.1038/s41598-022-16778-z
• Hutchinson IA, Shalloo L, Butler ST. Expanding the dairy herd in pasture-based systems: the role of sexed semen use in virgin heifers and lactating cows. J Dairy Sci. 2013;96(10):6742-52. http://dx.doi.org/10.3168/jds.2012-6476 PMid:23958011.
  » http://dx.doi.org/10.3168/jds.2012-6476
• Ibidhi R, Calsamiglia S. Carbon footprint assessment of Spanish dairy cattle farms: effectiveness of dietary and farm management practices as a mitigation strategy. Animals (Basel). 2020;10(11):2083. http://dx.doi.org/10.3390/ani10112083 PMid:33182611.
  » http://dx.doi.org/10.3390/ani10112083
• ISO. ISO 14040:2006(E): environmental management - life cycle assessment - principles and framework. Geneva: ISO; 2006a.
• ISO. ISO 14044:2006(E): environmental management - life cycle assessment - requirements and guidelines. Geneva: ISO; 2006b.
• Johnson KA, Johnson DE. Methane emissions from cattle. J Anim Sci. 1995;73(8):2483-92. http://dx.doi.org/10.2527/1995.7382483x PMid:8567486.
  » http://dx.doi.org/10.2527/1995.7382483x
• Kadzere CT, Murphy MR, Silanikove N, Maltz E. Heat stress in lactating dairy cows: a review. Livest Prod Sci. 2002;77(1):59-91. http://dx.doi.org/10.1016/S0301-6226(01)00330-X
  » http://dx.doi.org/10.1016/S0301-6226(01)00330-X
• Kasinathan P, Wei H, Xiang T, Molina JA, Metzger J, Broek D, Kasinathan S, Faber DC, Allan MF. Acceleration of genetic gain in cattle by reduction of generation interval. Sci Rep. 2015;5(1):8674. http://dx.doi.org/10.1038/srep08674 PMid:25728468.
  » http://dx.doi.org/10.1038/srep08674
• Kauffold J, Amer HAH, Bergfeld U, Müller F, Weber W, Sobiraj A. Offspring from non-stimulated calves at an age younger than two months: a preliminary report. J Reprod Dev. 2005;51(4):527-32. http://dx.doi.org/10.1262/jrd.17015 PMid:15976483.
  » http://dx.doi.org/10.1262/jrd.17015
• Kava R, Peripolli E, Brunes LC, Espigolan R, Mendes EDM, da Silva Neto JB, Londoño-Gil M, Sainz RD, Lobo RB, Baldi F. Estimates of genetic and phenotypic parameters for feeding behaviour and feed efficiency-related traits in Nelore cattle. J Anim Breed Genet. 2023;140(3):264-75. http://dx.doi.org/10.1111/jbg.12756 PMid:36633154.
  » http://dx.doi.org/10.1111/jbg.12756
• Kenny DA, Fitzsimons C, Waters SM, McGee M. Invited review: improving feed efficiency of beef cattle - the current state of the art and future challenges. Animal. 2018;12(9):1815-26. http://dx.doi.org/10.1017/S1751731118000976 PMid:29779496.
  » http://dx.doi.org/10.1017/S1751731118000976
• Knapp B Jr, Nordskog AW. Heritability of growth and efficiency in beef cattle. J Anim Sci. 1946;5(1):62-70. http://dx.doi.org/10.2527/jas1946.5162 PMid:21015595.
  » http://dx.doi.org/10.2527/jas1946.5162
• Knapp JR, Laur GL, Vadas PA, Weiss WP, Tricarico JM. Invited review: enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J Dairy Sci. 2014;97(6):3231-61. http://dx.doi.org/10.3168/jds.2013-7234 PMid:24746124.
  » http://dx.doi.org/10.3168/jds.2013-7234
• Kowalski LH, Fernandes SR, DiLorenzo N, Moletta JL, Rossi P, de Freitas JA. Residual feed intake and reproductive traits of growing Purunã bulls. J Anim Sci. 2017;95(2):930-8. http://dx.doi.org/10.2527/jas.2016.0888 PMid:28380596.
  » http://dx.doi.org/10.2527/jas.2016.0888
• Kyttä V, Roitto M, Astaptsev A, Saarinen M, Tuomisto HL. Review and expert survey of allocation methods used in life cycle assessment of milk and beef. Int J Life Cycle Assess. 2022;27(2):191-204. http://dx.doi.org/10.1007/s11367-021-02019-4
  » http://dx.doi.org/10.1007/s11367-021-02019-4
• Løvendahl P, Difford GF, Li B, Chagunda MGG, Huhtanen P, Lidauer MH, Lassen J, Lund P. Review: selecting for improved feed efficiency and reduced methane emissions in dairy cattle. Animal. 2018;12(S2):s336-49. http://dx.doi.org/10.1017/S1751731118002276 PMid:30255826.
  » http://dx.doi.org/10.1017/S1751731118002276
• Madilindi MA, Zishiri OT, Dube B, Banga CB. Technological advances in genetic improvement of feed efficiency in dairy cattle: A review. Livest Sci. 2022;258:104871. http://dx.doi.org/10.1016/j.livsci.2022.104871
  » http://dx.doi.org/10.1016/j.livsci.2022.104871
• Manzano P, Rowntree J, Thompson L, del Prado A, Ederer P, Windisch W, Lee MRF. Challenges for the balanced attribution of livestock’s environmental impacts: the art of conveying simple messages around complex realities. Anim Front. 2023;13(2):35-44. http://dx.doi.org/10.1093/af/vfac096.
  » https://doi.org/10.1093/af/vfac096
• Mapletoft RJ, Bó GA, Baruselli PS, Menchaca A, Sartori R. Evolution of knowledge on ovarian physiology and its contribution to the widespread application of reproductive biotechnologies in South American cattle. Anim Reprod. 2018;15(Suppl 1):1003-14. http://dx.doi.org/10.21451/1984-3143-AR2018-0007 PMid:36249848.
  » http://dx.doi.org/10.21451/1984-3143-AR2018-0007
• Montanholi YR, Fontoura ABP, Diel de Amorim M, Foster RA, Chenier T, Miller SP. Seminal plasma protein concentrations vary with feed efficiency and fertility-related measures in young beef bulls. Reprod Biol. 2016;16(2):147-56. http://dx.doi.org/10.1016/j.repbio.2016.04.002 PMid:27288339.
  » http://dx.doi.org/10.1016/j.repbio.2016.04.002
• Monteiro CMR, Biagi MB, Perri SHV, Carvalho RGD, Nogueira GDP. Desenvolvimento folicular em ovários de fetos zebuínos (Bos taurus indicus). Biotemas. 2009;22(3):185-91. http://dx.doi.org/10.5007/2175-7925.2009v22n3p185.
  » https://doi.org/10.5007/2175-7925.2009v22n3p185
• Mottet A, de Haan C, Falcucci A, Tempio G, Opio C, Gerber P. Livestock: on our plates or eating at our table? A new analysis of the feed/food debate. Glob Food Secur. 2017;14:1-8. http://dx.doi.org/10.1016/j.gfs.2017.01.001
  » http://dx.doi.org/10.1016/j.gfs.2017.01.001
• Mottet A, Teillard F, Boettcher P, Besi G, De Besbes B. Review: domestic herbivores and food security: current contribution, trends and challenges for a sustainable development. Animal. 2018;12(S2):s188-98. http://dx.doi.org/10.1017/S1751731118002215 PMid:30215340.
  » http://dx.doi.org/10.1017/S1751731118002215
• Mu Y, Vander Voort GV, Abo-Ismail MK, Ventura R, Jamrozik J, Miller SP. Genetic correlations between female fertility and postweaning growth and feed efficiency traits in multibreed beef cattle. Can J Anim Sci. 2016;96(3):448-55. http://dx.doi.org/10.1139/cjas-2015-0175
  » http://dx.doi.org/10.1139/cjas-2015-0175
• Nkrumah JD, Okine EK, Mathison GW, Schmid K, Li C, Basarab JA, Price MA, Wang Z, Moore SS. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J Anim Sci. 2006;84(1):145-53. http://dx.doi.org/10.2527/2006.841145x PMid:16361501.
  » http://dx.doi.org/10.2527/2006.841145x
• O’Brien D, Shalloo L, Grainger C, Buckley F, Horan B, Wallace M. The influence of strain of Holstein-Friesian cow and feeding system on greenhouse gas emissions from pastoral dairy farms. J Dairy Sci. 2010;93(7):3390-402. http://dx.doi.org/10.3168/jds.2009-2790 PMid:20630255.
  » http://dx.doi.org/10.3168/jds.2009-2790
• O’Hara E, Neves ALA, Song Y, Guan LL. The role of the gut microbiome in cattle production and health: drivers or passengers? Annu Rev Anim Biosci. 2020;8:199-220. http://dx.doi.org/10.1146/annurev-animal-021419-083952 PMid:32069435.
  » http://dx.doi.org/10.1146/annurev-animal-021419-083952
• Onuma H, Hahn J, Foote RH. Factors affecting superovulation, fertilization and recovery of superovulated ova in prepubertal cattle. J Reprod Fertil. 1970;21(1):119-26. http://dx.doi.org/10.1530/jrf.0.0210119 PMid:5413346.
  » http://dx.doi.org/10.1530/jrf.0.0210119
• Pinares-Patiño CS, Waghorn GC, Machmüller A, Vlaming B, Molano G, Cavanagh A, Clark H. Methane emissions and digestive physiology of non-lactating dairy cows fed pasture forage. Can J Anim Sci. 2007;87(4):601-13. http://dx.doi.org/10.4141/CJAS06023
  » http://dx.doi.org/10.4141/CJAS06023
• Randel RD, Welsh TH Jr. Interactions of feed efficiency with beef heifer reproductive development. J Anim Sci. 2013;91(3):1323-8. http://dx.doi.org/10.2527/jas.2012-5679 PMid:23048157.
  » http://dx.doi.org/10.2527/jas.2012-5679
• Rhoads ML, Rhoads RP, VanBaale MJ, Collier RJ, Sanders SR, Weber WJ, Crooker BA, Baumgard LH. Effects of heat stress and plane of nutrition on lactating Holstein cows: I. Production, metabolism, and aspects of circulating somatotropin. J Dairy Sci. 2009;92(5):1986-97. http://dx.doi.org/10.3168/jds.2008-1641 PMid:19389956.
  » http://dx.doi.org/10.3168/jds.2008-1641
• Roehe R, Dewhurst RJ, Duthie CA, Rooke JA, McKain N, Ross DW, Hyslop JJ, Waterhouse A, Freeman TC, Watson M, Wallace RJ. Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance. PLoS Genet. 2016;12(2):e1005846. http://dx.doi.org/10.1371/journal.pgen.1005846 PMid:26891056.
  » http://dx.doi.org/10.1371/journal.pgen.1005846
• Ross EM, Moate PJ, Marett L, Cocks BG, Hayes BJ. Investigating the effect of two methane-mitigating diets on the rumen microbiome using massively parallel sequencing. J Dairy Sci. 2013;96(9):6030-46. http://dx.doi.org/10.3168/jds.2013-6766 PMid:23871375.
  » http://dx.doi.org/10.3168/jds.2013-6766
• Sá MF Fo, Penteado L, Reis EL, Reis TANPS, Galvão KN, Baruselli PS. Timed artificial insemination early in the breeding season improves the reproductive performance of suckled beef cows. Theriogenology. 2013;79(4):625-32. http://dx.doi.org/10.1016/j.theriogenology.2012.11.016 PMid:23261306.
  » http://dx.doi.org/10.1016/j.theriogenology.2012.11.016
• Sarghale AJ, Shahrebabak MM, Shahrebabak HM, Javaremi AN, Saatchi M, Khansefid M, Miar Y. Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data. BMC Genet. 2020;21(1):129. http://dx.doi.org/10.1186/s12863-020-00953-0 PMid:33228565.
  » http://dx.doi.org/10.1186/s12863-020-00953-0
• Sartori R, Prata AB, Figueiredo ACS, Sanches BV, Pontes GCS, Viana JHM, Pontes JH, Vasconcelos JLM, Pereira MHC, Dode MAN, Monteiro PL Jr, Baruselli PS. Update and overview on assisted reproductive technologies (ARTs) in Brazil. Anim Reprod. 2016;13(3):300-12. http://dx.doi.org/10.21451/1984-3143-AR873
  » http://dx.doi.org/10.21451/1984-3143-AR873
• Schefers JM, Weigel KA. Genomic selection in dairy cattle: integration of DNA testing into breeding programs. Anim Front. 2012;2(1):4-9. http://dx.doi.org/10.2527/af.2011-0032
  » http://dx.doi.org/10.2527/af.2011-0032
• Seabury CM, Oldeschulte DL, Saatchi M, Beever JE, Decker JE, Halley YA, Bhattarai EK, Molaei M, Freetly HC, Hansen SL, Yampara-Iquise H, Johnson KA, Kerley MS, Kim JW, Loy DD, Marques E, Neibergs HL, Schnabel RD, Shike DW, Spangler ML, Weaber RL, Garrick DJ, Taylor JF. Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle. BMC Genomics. 2017;18(1):386. http://dx.doi.org/10.1186/s12864-017-3754-y PMid:28521758.
  » http://dx.doi.org/10.1186/s12864-017-3754-y
• St-Pierre NR, Cobanov B, Schnitkey G. Economic losses from heat stress by US livestock industries. J Dairy Sci. 2003;86(S1):E52-77. http://dx.doi.org/10.3168/jds.S0022-0302(03)74040-5
  » http://dx.doi.org/10.3168/jds.S0022-0302(03)74040-5
• 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(12):100673. http://dx.doi.org/10.1016/j.animal.2022.100673 PMid:36402112.
  » http://dx.doi.org/10.1016/j.animal.2022.100673
• 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(11):3175. http://dx.doi.org/10.3390/ani11113175 PMid:34827907.
  » http://dx.doi.org/10.3390/ani11113175
• Terry SA, Basarab JA, Guan LL, McAllister TA. Strategies to improve the efficiency of beef cattle production. Can J Anim Sci. 2021;101(1):1-19. http://dx.doi.org/10.1139/cjas-2020-0022
  » http://dx.doi.org/10.1139/cjas-2020-0022
• Thompson L, Rowntree J, Windisch W, Waters SM, Shalloo L, Manzano P. Ecosystem management using livestock: embracing diversity and respecting ecological principles. Anim Front. 2023;13(2):28-34. http://dx.doi.org/10.1093/af/vfac094 PMid:37073311.
  » http://dx.doi.org/10.1093/af/vfac094
• Vallimont JE, Dechow CD, Daubert JM, Dekleva MW, Blum JW, Liu W, Varga GA, Heinrichs AJ, Baumrucker CR. Short communication: feed utilization and its associations with fertility and productive life in 11 commercial Pennsylvania tie-stall herds. J Dairy Sci. 2013;96(2):1251-4. http://dx.doi.org/10.3168/jds.2012-5712 PMid:23219114.
  » http://dx.doi.org/10.3168/jds.2012-5712
• Vries M, van Middlaar CE, de Boer IJM. Comparing environmental impacts of beef production systems: a review of life cycle assessments. Livest Sci. 2015;178:279-88. http://dx.doi.org/10.1016/j.livsci.2015.06.020
  » http://dx.doi.org/10.1016/j.livsci.2015.06.020
• Wallace RJ, Rooke JA, McKain N, Duthie CA, Hyslop JJ, Ross DW, Waterhouse A, Watson M, Roehe R. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics. 2015;16(1):839. http://dx.doi.org/10.1186/s12864-015-2032-0 PMid:26494241.
  » http://dx.doi.org/10.1186/s12864-015-2032-0
• Zetouni L, Kargo M, Norberg E, Lassen J. Genetic correlations between methane production and fertility, health, and body type traits in Danish Holstein cows. J Dairy Sci. 2018;101(3):2273-80. http://dx.doi.org/10.3168/jds.2017-13402 PMid:29331458.
  » http://dx.doi.org/10.3168/jds.2017-13402

Publication Dates

• Publication in this collection
  08 Sept 2023
• Date of issue
  2023

History

• Received
  16 May 2023
• Accepted
  31 July 2023