Performance Evaluation of Highly Admixed Tanzanian Smallholder Dairy Cattle Using SNP Derived Kinship Matrix.

BLUP EBV SNP admixture cluster dairy performance smallholder

Journal

Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621

Informations de publication

Date de publication:
2019
Historique:
received: 25 04 2018
accepted: 09 04 2019
entrez: 21 5 2019
pubmed: 21 5 2019
medline: 21 5 2019
Statut: epublish

Résumé

The main purpose of this study was to understand the type of dairy cattle that can be optimally used by smallholder farmers in various production environments such that they will maximize their yields without increasing the level of inputs. Anecdotal evidence and previous research suggests that the optimal level of taurine inheritance in crossbred animals lies between 50 and 75% when considering total productivity in tropical management clusters. We set out to assess the relationship between breed composition and productivity for various smallholder production systems in Tanzania. We surveyed 654 smallholder dairy households over a 1-year period and grouped them into production clusters. Based on supplementary feeding, milk productivity and sale as well as household wealth status four clusters were described: low-feed-low-output subsistence, medium-feed-low-output subsistence, maize germ intensive semi-commercial and feed intensive commercial management clusters. About 839 crossbred cows were genotyped at approximately 150,000 single nucleotide polymorphism (SNP) loci and their breed composition determined. Percentage dairyness (proportion of genes from international dairy breeds) was estimated through admixture analysis with Holstein, Friesian, Norwegian Red, Jersey, Guernsey, N'Dama, Gir, and Zebu as references. Four breed types were defined as RED-GUE (Norwegian Red/Friesian-Guernsey; Norwegian Red/Friesian-Jersey), RED-HOL (Norwegian Red/Friesian-Holstein), RED-Zebu (Norwegian Red/Friesian-Zebu), Zebu-RED (Zebu-Norwegian Red/Friesian) based on the combination of breeds that make up the top 76% breed composition. A fixed regression model using a genomic kinship matrix was used to analyze milk yield records. The fitted model accounted for year-month-test-date, parity, age, breed type and the production clusters as fixed effects in the model in addition to random effects of animal and permanent environment effect. Results suggested that RED-Zebu breed type with dairyness between 75 and 85% is the most appropriate for a majority of smallholder management clusters. Additionally, for farmers in the feed intensive management group, animals with a Holstein genetic background with at least 75% dairy composition were the best performing. These results indicate that matching breed type to production management group is central to maximizing productivity in smallholder systems. The findings from this study can serve as a basis to inform the development of the dairy sector in Tanzania and beyond.

Identifiants

pubmed: 31105745
doi: 10.3389/fgene.2019.00375
pmc: PMC6498096
doi:

Types de publication

Journal Article

Langues

eng

Pagination

375

Références

J Dairy Sci. 2016 Sep;99(9):7308-7312
pubmed: 27423951
Trop Anim Health Prod. 2000 Feb;32(1):23-31
pubmed: 10717941
J Dairy Sci. 2018 Oct;101(10):9108-9127
pubmed: 30077450
J Anim Breed Genet. 2010 Oct;127(5):348-51
pubmed: 20831558
Am J Hum Genet. 2007 Sep;81(3):559-75
pubmed: 17701901
J Dairy Sci. 2003 Mar;86(3):1036-44
pubmed: 12703641
Genome Res. 2009 Sep;19(9):1655-64
pubmed: 19648217
J Dairy Sci. 1999 Oct;82(10):2231-7
pubmed: 10531612
J Dairy Sci. 2008 Nov;91(11):4414-23
pubmed: 18946147
Genet Sel Evol. 2017 Sep 12;49(1):67
pubmed: 28899355

Auteurs

Fidalis D N Mujibi (FDN)

USOMI Limited, Nairobi, Kenya.
Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.

James Rao (J)

International Livestock Research Institute, Nairobi, Kenya.

Morris Agaba (M)

Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.

Devotha Nyambo (D)

Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.

Evans K Cheruiyot (EK)

USOMI Limited, Nairobi, Kenya.
Department of Animal Production, College of Agriculture and Veterinary Sciences, University of Nairobi, Nairobi, Kenya.

Absolomon Kihara (A)

International Livestock Research Institute, Nairobi, Kenya.
Badili Innovations Limited, Nairobi, Kenya.

Yi Zhang (Y)

College of Animal Science and Technology, China Agricultural University, Beijing, China.

Raphael Mrode (R)

International Livestock Research Institute, Nairobi, Kenya.
Scotland's Rural College, Edinburgh, United Kingdom.

Classifications MeSH