Download full text in PDF

Ruban SY, Danshyn VO. Feed efficiency of dairy cattle as genetic trait. Bìol Tvarin. 2024; 26 (1): 3–10.
https://doi.org/10.15407/animbiol26.01.003
Received 16.09.2023 ▪ Revision 09.02.2024 ▪ Accepted 13.03.2024 ▪ Published online 29.03.2024

---

Feed efficiency of dairy cattle as genetic trait

S. Y. Ruban, V. O. Danshyn

This email address is being protected from spambots. You need JavaScript enabled to view it.

National University of Life and Environmental Sciences of Ukraine, 15 Heroiv Oborony str., Kyiv 03041, Ukraine

---

This review article is devoted to the use of feed efficiency traits in dairy cattle breeding. An efficient cow is defined as the one that produces the same amount of milk and milk solids while consuming less feed and remaining healthy and fertile; thus, allowing to reduce costs without decrease in production. Improving feed efficiency is economically important due to the increasing price of fodder. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. Nowadays genetic evaluation of dairy cattle for feed efficiency is routinely conducted in several countries, including Australia, USA, Canada, Netherlands, Denmark, Sweden, Finland, Norway and United Kingdom. Different countries use different measures of feed efficiency of dairy cows. The main feed efficiency traits are dry matter intake, gross feed efficiency, residual feed intake, energy balance and feed saved. Genome-wide association studies demonstrated that feed efficiency in polygenic trait. Nevertheless, several genes with large effects on feed efficiency were identified. Estimates of heritability of these traits vary from 0.07 to 0.49 and show the presence of considerable genetic variation of these traits and therefore, the possibility of their genetic improvement under the conditions of inclusion in breeding programs. Changes in diet and rumen microbiome substantially impact feed efficiency of dairy cows. Feed efficiency is related to methane emissions and excess nitrogen excretion. Genetic improvement of feed efficiency requires recording of individual data on feed intake in cows. Such data are limited. Two options exist to solve this problem: use of indirect predictors and genomic prediction. Accuracy of genomic prediction varies from 0.21 to 0.61 across countries. International cooperative projects such as Efficient Dairy Genome Project in Canada were launched to establish large databases and to increase accuracy of feed efficiency traits genomic prediction. Future directions of research are the use of novel technologies: mid-infrared spectroscopy, artificial intelligence, holo-omics.

Key words: dry matter intake, energy balance, residual feed intake, feed saved, heritability, genomic selection, holo-omics

---

1. Becker VAE, Stamer E, Spiekers H, Thaller G. Genetic parameters for dry matter intake, energy balance, residual energy intake, and liability to diseases in German Holstein and Fleckvieh dairy cows. J Dairy Sci. 2022; 105 (12): 9738–9750. DOI: 10.3168/jds.2022-22083.
2. Bolormaa S, MacLeod IM, Khansefid M, Marett LC, Wales WJ, Nieuwhof GJ, Baes CF, Schenkel FS, Goddard M E, Pryce JE. Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle. JDS Commun. 2022; 3 (2): 114–119. DOI: 10.3168/jdsc.2021-0150.
3. Bolormaa S, MacLeod IM, Khansefid M, Marett LC, Wales WJ, Miglior F, Baes CF, Schenkel FS, Connor EE, Manzanilla-Pech CIV, Stothard P, Herman E, Nieuwhof GJ, Goddard ME, Pryce JE. Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency. Genet Sel Evol. 2022; 54: 60. DOI: 10.1186/s12711-022-00749-z.
4. Brito LF, Oliveira HR, Houlahan K, Fonseca PAS, Lam S, Butty AM, Seymour DJ, Vargas G, Chud TCS, Silva FF, Baes CF, Cánovas A, Miglior F, Schenkel FS. Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle. Canad J Anim Sci. 2020; 100 (4): 587–604. DOI: 10.1139/cjas-2019-0193.
5. Brown WE, Cavani L, Peñagaricano F, Weigel KA, White HM. Feeding behavior parameters and temporal patterns in mid-lactation Holstein cows across a range of residual feed intake values. J Dairy Sci. 2022; 105 (10): 8130–8142. DOI: 10.3168/jds.2022-22093.
6. Cantalapiedra-Hijar G, Dewhurst RJ, Cheng L, Cabrita ARJ, Fonseca AJM, Nozière P, Makowski D, Fouillet H, Ortigues-Marty I. Nitrogen isotopic fractionation as a biomarker for nitrogen use efficiency in ruminants: A meta-analysis. Animal. 2018; 12 (9): 1827–1837. DOI: 10.1017/S1751731117003391.
7. Cavani L, Brown WE, Parker Gaddis KL, Tempelman RJ, VandeHaar MJ, White HM, Peñagaricano F, & Weigel KA. Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows. J Dairy Sci. 2022; 105 (9): 7564–7574. DOI: 3168/jds.2022-22066.
8. Chen Y, Vanderick S, Mota RR, Grelet C, GplusE Consortium, Gengler N. Estimation of genetic parameters for predicted nitrogen use efficiency and losses in early lactation of Holstein cows. J Dairy Sci. 2021; 104 (4): 4413–4423. DOI: 10.3168/jds.2020-18849.
9. Chen Y, Atashi H, Grelet C, Vanderick S, Hu H, Gengler N. Defining a nitrogen efficiency index in Holstein cows and assessing its potential effect on the breeding program of bulls. J Dairy Sci. 2022; 105 (9): 7575–7587. DOI: 10.3168/jds.2021-21681.
10. Chen Y, Atashi H, Grelet C, Mota RR, Vanderick S, Hu H, GplusE Consortium, Gengler N. Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle. J Dairy Sci. 2023; 106 (5): 3397–3410. DOI: 10.3168/jds.2022-22351.
11. Coffey M. Advances in dairy cattle breeding to incorporate feed conversion efficiency in national genetic evaluations. In: Advances in breeding of dairy cattle. Ed. by J. van der Werf, J. Pryce. Burleigh Dodds Science Publishing Limited, 2020: 173–189. DOI: 10.19103/AS.2019.0058.11.
12. De Jong G, Bouwmeester-Vosman JJ, van der Linde C, de Haas Y, Schopen GCB, Veerkamp RF. Feed intake genetic evaluation: progress and an index for saved feed cost. Proceedings of the 2019 Interbull Meeting, Cincinnati, OH (USA), 23–26 June 2019. Interbull Bull. 2019; 55. Available at: https://journal.interbull.org/index.php/ib/article/view/171
13. Delgado B, Bach A, Guasch I, González C, Elcoso G, Pryce JE, Gonzalez-Recio O. Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Sci Rep. 2019; 9: 11. DOI: 10.1038/s41598-018-36673-w.
14. Dórea JRR, Rosa GJM, Weld KA, Armentano LE. Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows. J Dairy Sci. 2018; 101 (7): 5878–5889. DOI: 10.3168/jds.2017-13997.
15. Fischer A, Dai X, Kalscheur KF. Feed efficiency of lactating Holstein cows is repeatable within diet but less reproducible when changing dietary starch and forage concentrations. Animal. 2022; 16 (8): 100599. DOI: 10.1016/j.animal.2022.100599.
16. Hayes B. Genomes, phenomes and microbiomes to improve health, welfare and productivity of livestock. The University of Queensland, Australia, 2019: 25 p.
17. Houlahan K, Schenkel FS, Hailemariam D, Lassen J, Kargo M, Cole JB, Connor EE, Wegmann S, Oliveira Júnior GA, Miglior F, Fleming A, Chud TCS, Baes CF. Effects of incorporating dry matter intake and residual feed intake into a selection index for dairy cattle using deterministic modeling. Animals. 2021; 11 (4): 1157. DOI: 10.3390/ani11041157.
18. Karlsson J, Danielsson R, Åkerlind M, Holtenius K. Full-lactation performance of multiparous dairy cows with differing residual feed intake. PLoS ONE. 2022; 17 (8): e0273420. DOI: 10.1371/journal.pone.0273420.
19. Khanal P, Parker Gaddis KL, Vandehaar MJ, Weigel KA, White HM, Peñagaricano F, Koltes JE, Santos JEP, Baldwin RL, Burchard JF, Dürr JW, Tempelman RJ. Multiple-trait random regression modeling of feed efficiency in US Holsteins. J Dairy Sci. 2022; 105 (7): 5954–5971. DOI: 10.3168/jds.2021-21739.
20. Krattenmacher N, Thaller G, Tetens J. Analysis of the genetic architecture of energy balance and its major determinants dry matter intake and energy-corrected milk yield in primiparous Holstein cows. J Dairy Sci. 2019; 102 (4): 3241–3253. DOI: 10.3168/jds.2018-15480.
21. Lam S, Miglior F, Fonseca PAS, Gómez-Redondo I, Zeidan J, Suárez-Vega A, Schenkel F, Guan LL, Waters S, Stothard P, Cánovas A. Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing. J Dairy Sci. 2021; 104 (2): 1928–1950. DOI: 10.3168/jds.2020-18241.
22. Lassen J, Thomasen JR, Hansen RH, Nielsen GGB, Olsen E, Stentebjerg PRB, Hansen NW, Borchersen S. Individual measures of feed intake on in-house commercial dairy cattle using 3D camera technology. World Congress on Genetics Applied to Livestock Production. Auckland, New Zealand, 2018: 635.
23. Li B, Fang L, Null DJ, Hutchison JL, Connor EE, VanRaden PM, VandeHaar MJ, Tempelman RJ, Weigel KA, Cole JB. High-density genome-wide association study for residual feed intake in Holstein dairy cattle. J Dairy Sci. 2019; 102 (12): 11067–11080. DOI: 10.3168/jds.2019-16645.
24. Li F, Li C, Chen Y, Liu J, Zhang C, Irving B, Fitzsimmons C, Plastow G, Guan LL. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome. 2019; 7: 92. DOI: 10.1186/s40168-019-0699-1.
25. Madilindi MA, Zishiri OT, Dube B, Banga CB. Technological advances in genetic improvement of feed efficiency in dairy cattle: A review. Livestock Sci. 2022; 258: 104871. DOI: 10.1016/j.livsci.2022.104871.
26. Manzanilla-Pech CIV, Løvendahl 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 (8): 8983–9001. DOI: 10.3168/jds.2020-19889.
27. Manzanilla-Pech CIV, Stephansen RB, Difford GF, Løvendahl P, Lassen J. Selecting for feed efficient cows will help to reduce methane gas emissions. Front Gen. 2022; 13: 885932. DOI: 10.3389/fgene.2022.885932.
28. Martin MJ, Dórea JRR, Borchers MR, Wallace RL, Bertics SJ, DeNise SK, Weigel KA, White HM. Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. J Dairy Sci. 2021; 104 (8): 8765–8782. DOI: 10.3168/jds.2020-20051.
29. McGovern E, Kenny DA, McCabe MS, Fitzsimons C, McGee M, Kelly AK, Waters SM. 16S rRNA sequencing reveals relationship between potent cellulolytic genera and feed efficiency in the rumen of bulls. Front Microbiol. 2018; 9: 1842. DOI: 10.3389/fmicb.2018.01842.
30. Miglior F, Baes C, Cánovas A, Coffey M, Connor E, De Pauw M, Goddard E, Hailu G, Lassen J, Malchiodi F, Osborne V, Pryce J, Sargolzaei M, Schenkel F, Wall E, Wang Z, Wegman S, Wright T, Stothard P Progress report for the Efficient Dairy Genome Project. J Anim Sci. 2018; 96 (3): 123. DOI: 10.1093/jas/sky404.271.
31. Montenegro JD. Gene Co-expression network analysis. In: Edwards D. (ed.). Plant Bioinformatics: Methods and Protocols. Method Mol Biol. 2022; 2443: 387–404. DOI: 10.1007/978-1-0716-2067-0_19.
32. Nadri S, Sadeghi-Sefidmazgi A, Zamani P, Ghorbani GR, Toghiani S. Implementation of feed efficiency in Iranian Holstein Breeding Program. Animals. 2023; 13 (7): 1216. DOI: 10.3390/ani13071216.
33. Nehme Marinho M, Zimpel R, Peñagaricano F, Santos JEP. Assessing feed efficiency in early and mid lactation and its associations with performance and health in Holstein cows. J Dairy Sci. 2021; 104 (5): 5493–5507. DOI: 10.3168/jds.2020-19652.
34. Nehme Marinho M, Santos JEP. Association of residual feed intake with blood metabolites and reproduction in Holstein cows. Front Anim Sci. 2022; 3: 847574. DOI: 10.3389/fanim.2022.847574.
35. Parker Gaddis KL, VanRaden PM, Tempelman RJ, Weigel KA, White HM, Peñagaricano F, Koltes JE, Santos JEP, Baldwin RL, Burchard JF, Dürr JW, VandeHaar MJ. Implementation of Feed Saved evaluations in the U.S. Proceedings of the 2021 Interbull Meeting, Leeuwarden (Netherlands), April 26–30, 2021. Interbull Bull. 2021; 56: 147–152. Available at: https://journal.interbull.org/index.php/ib/article/view/72
36. Price of Feed. IBISWorld, published April 23, 2024. Available at: https://www.ibisworld.com/us/bed/price-of-feed/745
37. Pryce JE, Nguyen TTT, Axford M, Nieuwhof G, Shaffer M. Symposium review: Building a better cow — the Australian experience and future perspectives. J Dairy Sci. 2018; 101 (4): 3702–3713. DOI: 10.3168/jds.2017-13377.
38. Qadri QR, Zhao Q, Lai X, Zhang Z, Zhao W, Pan Y, Wang Q. Estimation of complex-trait prediction accuracy from the different holo-omics interaction models. Genes. 2022; 13 (9): 1580. DOI: 10.3390/genes13091580.
39. Richardson CM, Baes CF, Amer PR, Quinton C, Martin P, Osborne VR, Pryce JE, Miglior F. Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle. Animal. 2020; 14 (1): 171–179. DOI: 10.1017/S175173111900154X.
40. Ruban SY, Perekrestova AV, Shablia VP, Bochkov VM. Feed conversion efficiency in different groups of dairy cows. Ukr J Ecol. 2018; 8 (1): 124–129. DOI: 10.15421/2018_196.
41. Ruban SY, Danshin VO, Fedota AM. Possibilities of application of feed efficiency and reproduction traits in dairy cattle breeding of Ukraine. Anim Sci Food Tech. 2019; 10 (3): 41–55. DOI: 10.31548/animal2019.03.041. (in Ukrainian)
42. Ruban SY, Danshin VO, Kyrii AA. Genetic improvement of dairy cattle for feed efficiency and fertility. Dynamics of the development of world science: VII International scientific and practical conference, Vancouver (Canada), March 18–20, 2020: 157–162.
43. Ruban S, Danshin V. Perspectives for the use of genomic selection for genetic improvement of dairy cattle in Ukraine. Ukr Black Sea Reg Agr Sci. 2023; 27 (1): 20–29. DOI: 10.56407/bs.agrarian/1.2023.20.
44. Salleh SM, Mazzoni G, Løvendahl P, Kadarmideen HN. Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency. BMC Bioinform. 2018; 19: 513. DOI: 10.1186/s12859-018-2553-z.
45. Seymour DJ, Cánovas A, Baes CF, Chud TCS, Osborne VR, Cant JP, Brito LF, Gredler-Grandl B, Finocchiaro R, Veerkamp RF, de Haas Y, Miglior F. Invited review: Determination of large-scale individual dry matter intake phenotypes in dairy cattle. J Anim Sci. 2019; 102 (9): 7655–7663. DOI: 10.3168/jds.2019-16454.
46. Thomasen JR, Lassen J, Nielsen GGB, Borggard C, Stentebjerg PRB, Hansen RH, Hansen NW, Borchersen S. Individual cow identification in a commercial herd using 3D camera technology. World Congress on Genetics Applied to Livestock Production. Auckland (New Zealand), 2018: 613.
47. VanRaden PM, Cole J, Neupane M, Toghiani S, Parker Gaddis KL, Tempelman RJ. Net merit as a Measure of Lifetime Profit: 2021 Revision. AIP RESEARCH REPORT NM$8 (05-21). 2021: 20 p. Available at: https://www.ars.usgov/ARSUserFiles/80420530/Publications/ARR/nmcalc-2021_ARR-NM8.pdf
48. Wallace RJ, Sasson G, Garnsworthy PC, Tapio I, Gregson E, Bani P, Huhtanen P, Bayat AR, Strozzi F, Biscarini F, Snelling TJ, Saunders N, Potterton SL, Craigon J, Minuti A, Trevisi E, Callegari ML, Piccioli Cappelli F, Cabezas-Garcia EH, Vilkki J, Pinares-Patino C, Fliegerová KO, Mrázek J, Sechovcová H, Kopečný J, Bonin A, Boyer F, Taberlet P, Kokou F, Halperin E, Williams JL, Shingfield KJ, Mizrahi I. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv. 2019; 5 (7): eaav8391. DOI: 10.1126/sciadv.aav8391.
49. Zhang L, Gengler N, Dehareng F, Colinet F, Froidmont E, Soyeurt H. Can we observe expected behaviors at large and individual scales for feed efficiency-related traits predicted partly from milk mid-infrared spectra? Animals. 2020; 10(5): 873. DOI: 10.3390/ani10050873.