The DOI matches its Crossref record; 133 of the 133 references it cites were found in a registry or an index.
Strategies to improve the efficiency of beef cattle production
Open accessAbstract
Globally, there are approximately one billion beef cattle, and compared with poultry and swine, beef cattle have the poorest conversion efficiency of feed to meat. However, these metrics fail to consider that beef cattle produce high-quality protein from feeds that are unsuitable for other livestock species. Strategies to improve the efficiency of beef cattle are focusing on operational and breeding management, host genetics, functional efficiency of rumen and respiratory microbiomes, and the structure and composition of feed. These strategies must also consider the health and immunity of the herd as well as the need for beef cattle to thrive in a changing environment. Genotyping can identify hybrid vigor with positive consequences for animal health, productivity, and environmental adaptability. The role of microbiome–host interactions is key in efficient nutrient digestion and host health. Microbial markers and gene expression patterns within the rumen microbiome are being used to identify hosts that are efficient at fibre digestion. Plant breeding and processing are optimizing the feed value of both forages and concentrates. Strategies to improve the efficiency of cattle production are a prerequisite for the sustainable intensification needed to satisfy the future demand for beef.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, genetic manipulation of rumen microbiota is also possible through different techniques as host genetics influences some heritable microbial traits (81–83).”
From Phytogenic Additives Can Modulate Rumen Microbiome to Mediate Fermentation Kinetics and Methanogenesis Through Exploiting Diet–Microbe Interaction (2020) · cites this paper as [83]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Energy contributions of volatile fatty acids from the gastrointestinal tract in various species
Bergman, E. N. (1990) · Physiological Reviews · doi:10.1152/physrev.1990.70.2.567 · Cited by 2,750 · cited 1×
“This suggests that the observed variation in the abundance of a particular bacterial taxa can lead to varied production of short-chain fatty acids, which supply up to 70% of energy to the animal (Bergman 1990).”
BackgroundJohnson, K. A., & Johnson, D. E. (1995) · Journal of Animal Science · doi:10.2527/1995.7382483x · Cited by 2,714 · cited 2×
“Stoichiometry calculations suggest that enteric CH4 production represents an energetic loss in ruminants of 2%–12% of gross energy intake (Johnson and Johnson 1995).”
BackgroundHenderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Collaborators, G. R. C., Abecia, L., Angarita, E., Aravena, P., Nora Arenas, G., et al. (2015) · Scientific Reports · doi:10.1038/srep14567 · Cited by 1,682 · cited 3×
“However, given that the composition of the microbiome is overwhelmingly determined by the nature of the diet (Henderson et al. 2015) and the apparent variation in the rumen microbial indicators associated with RFI, their usefulness is yet to be proven.”
BackgroundInfluentialSelf-citationEfficiency of Feed Use in Beef Cattle
Koch, R. M., Swiger, L. A., Chambers, D., & GREGORY, K. E. (1963) · Journal of Animal Science · doi:10.2527/jas1963.222486x · Cited by 1,304 · cited 1×
“Koch et al. (1963) defined RFI in youthful cattle as the difference between actual feed consumed and predicted feed intake based on maintenance and growth.”
BackgroundNutrient Requirements of Beef Cattle, 7th ed
Gibb, D. J. (1997) · Europe PMC (PubMed Central) · Cited by 1,143
Livestock: On our plates or eating at our table? A new analysis of the feed/food debate
Mottet, A., de Haan, C., Falcucci, A., Tempio, G., Opio, C., & Gerber, P. (2017) · Global Food Security · doi:10.1016/j.gfs.2017.01.001 · Cited by 1,034 · cited 7×
“Although FCR is a superior measurement over average daily gain, FCR metrics do not account for the nutritional value of feed consumed or the human nutritional value placed on the output gain (Mottet et al. 2017).”
BackgroundInfluentialGrowth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005
Zuidhof, M. J., Schneider, B. L., Carney, V. L., Korver, D. R., & Robinson, F. E. (2014) · Poultry Science · doi:10.3382/ps.2014-04291 · Cited by 862
Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.
Shabat, S. K. B., Sasson, G., Doron-Faigenboim, A., Durman, T., Yaacoby, S., Berg Miller, M. E., White, B. A., Shterzer, N., & Mizrahi, I. (2016) · The ISME Journal · doi:10.1038/ismej.2016.62 · Cited by 823 · cited 6×
“Coprococcus catus was enriched in the rumen of efficient dairy cows that exhibited increased propionate and butyrate production, a shift in fermentation that may reduce the availability of metabolic hydrogen to reduce carbon dioxide to CH4 (Shabat et al. 2016).”
BackgroundInfluentialExtracellular Vesicles Isolated from the Leaf Apoplast Carry Stress-Response Proteins
Rutter, B. D., & Innes, R. W. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.01253 · Cited by 663 · cited 1×
“For example, exosomes in the apoplasts of Arabidopsis leaves were shown to be enriched in defense and stress-related proteins as a result of exposure to abiotic stress (Rutter and Innes 2017).”
BackgroundTeymouri, F., Laureano-Pérez, L., Alizadeh, H., & Dale, B. E. (2005) · Bioresource Technology · doi:10.1016/j.biortech.2005.01.016 · Cited by 551 · cited 3×
“AFEX involves exposing recalcitrant fibre to steam and anhydrous ammonia at high pressure and temperature for 1 h, followed by rapid depressurization and ammonia recovery (Teymouri et al. 2005).”
Background
Research works printed but not linked: 5
- Crews D.H., 2005, Genet. Mol. Res., 4, 152
- Mallard B.A., 2015, Jpn. J. Vet. Res., 63, S37
- Mandal S., 2017, Int. J. Drug Res. Technol., 6, 43
- Mertens D., 2008, J. Dairy Sci., 91
- Weakley D., 2008, J. Dairy Sci., 91
Cited by
Sorted by citations
Hassan, F.-U., Arshad, M. A., Ebeid, H. M., Rehman, M. S.-U., Khan, M. S., Shahid, S., & Yang, C. (2020) · Frontiers in Veterinary Science · doi:10.3389/fvets.2020.575801 · Cited by 186
New insights in improving sustainability in meat production: opportunities and challenges
Kumar, P., Abubakar, A. A., Verma, A. K., Umaraw, P., Ahmed, M. A., Mehta, N. R., Hayat, M. N., Kaka, U., & Sazili, A. Q. (2022) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2022.2096562 · Cited by 119
“…monogastric by producing 1 kg meat by consuming 2.8 kg food as compared to monogastric producing the same quantity of meat by consuming 3.2 kg food (Terry et al. 2020, 2021), but if we consider the volume of dry matter consumed, then ruminants produce 1 kg animal protein by consuming 133 kg dry…”
BackgroundMethodInfluentialNon-contact sensing technology enables precision livestock farming in smart farms
Yin, M., Ma, R., Luo, H., Li, J., Zhao, Q., & Zhang, M. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108171 · Cited by 114
Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
Li, G., Erickson, G. E., & Xiong, Y. (2022) · Animals · doi:10.3390/ani12111453 · Cited by 98
“Numerous challenges face the beef industry including needs for improved nutrition and production efficiency to feed a growing human population [1,2].”
BackgroundPulina, G., Acciaro, M., Atzori, A. S., Battacone, G., Crovetto, G. M., Mele, M. L., Pirlo, G., & Rassu, S. P. G. (2021) · animal · doi:10.1016/j.animal.2021.100358 · Cited by 69
Metadata
- Authors
- Terry, S. A., Basarab, J. A., Guan, L. L., & McAllister, T. A.
- Affiliations
- Agriculture and Agri-Food Canada (CA)
- Lacombe Research and Development Centre (CA)
- University of Alberta (CA)
- Venue
- Canadian Journal of Animal Science
- Publisher
- Canadian Science Publishing
- Published
- 2021-03-01
- Volume
- 101
- Issue
- 1
- Pages
- 1-19
- ISSN
- 0008-3984 (ISSN-L)
- 1918-1825
- OpenAlex
- W3045238561
- Cited by
- 142
- References
- 133
- Citations by year
- 2026: 20 · 2025: 37 · 2024: 33 · 2023: 28 · 2022: 19 · 2021: 4
- FWCI
- 16.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Canadian Journal of Animal Science (publishedVersion, cc-by)
- TSpace (University of Toronto) (acceptedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Ruminant Nutrition and Digestive Physiology
- Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Livestock Farming and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- feed efficiency, rumen microbiome, airway microbiome, host genetics, heterosis, genotyping, fiber digestion, forage breeding, feed processing, microbial biomarkers, gene expression, sustainable intensification, beef demand
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
