5 research outputs found
Ticks, demographics, vegetation cover, and farmer’s age influence red meat production in resource-poor regions of Eastern Cape Province of South Africa
This study presents an integrated examination of livestock production constraints
associating with communal farming in six district municipalities
(DMs) of the Eastern Cape Province. We collected data on demographic and
socio-economic factors from 271 farmers randomly given questionnaires.
About 26 Land Redistribution for Agricultural Development (LRAD) owned
by communal farmers were surveyed to ascertain the condition of grazing
land, meanwhile, tick species and distribution on cattle and goats found in
158 sampling sites of the study area were also recorded. From the results, a
total of 34,929 adult ticks belonging to five genera and 10 species were encountered.
The most abundant tick genera were Rhipicephalus of the subgenus
Boophilus (68.91%), Amblyomma (20.72%), Hyalomma (8.64%), Ixodes
(1.22%), and Haemaphysalis (0.51%). By geographic distributions, Am-blyomma and Rhipicephalus were common to all DMs, while R. microplus
and R. decoloratus were found in the drier regions. From the map plotted for
16 custom feeding centers, lack of marketing channels (23%), poor animal
conditions (20%), lack of infrastructure (19%), high price of medicine (14%),
shortage of feed (10%), stock theft (8%), and age of animals (too old) to be
marketed (6%) are the major shortcomings in red meat production. We
found that the associations between gender (χ 2 = 31.3481, p < 0.0001), age (χ 2
= 32.4889, p < 0.0001), and farming experience (χ 2 = 52.7556, p < 0.0001)
were significantly (p < 0.05) higher. Additionally, we found that farming
commodities were significantly influenced by gender and farming experience.
From the surveyed LRAD farms, we observed a higher proportion of increaser
II grass species in Alfred Nzo (55.6%), Amathole (53.9%), and Chris Hani
(46.4%) DMs. On the other hand, the decreaser grass species were few in Alfred
Nzo (16.5%), Amathole (13.8%), and Chris Hani (21.8%). Inferences
from the data indicate the need for government and stakeholders’ intervention
to farmers through the provision of infrastructures, marketing channels,
and training on livestock based programs.http://www.scirp.org/journal/ojasam2022Veterinary Tropical Disease
Bridging the gap in African biodiversity genomics and bioinformatics:Open Institute of the African BioGenome Project:
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Open Institute of the African BioGenome Project: Bridging the gap in African biodiversity genomics and bioinformatics
The Genetic Assessment of South African Nguni Sheep Breeds Using the Ovine 50K Chip
The indigenous sheep breeds of South Africa, such as the Nguni, are well adapted to different ecological regions throughout the country. This has resulted into different ecotypes. However, it is not clear if the differences among Nguni sheep are genetically distinct. The present study aimed to use the latest technology to assess the genetic relationship between Nguni sheep ecotypes and the relationship to other selected South African breeds using SNP markers. In the current study, 144 South African sheep samples (75 Nguni sheep and 69 mixed-breed sheep as a reference) were genotyped using the OvineSNP50 Bead Chip assay from Illumina. The Nguni consisted of 25 Pedi, 25 Swazi, and 25 Zulu sheep, with the reference group comprising 25 Namaqua, 23 Dorper, and 21 Damara sheep. After quality control of 54,241 SNPs, 48,429 SNPs remained for analysis (MAF > 0.05). There were genetic differences in the Nguni sheep population; notably, the Zulu and Swazi populations clustered together, but with a clear distinction from the Pedi ecotype. Genetic admixtures were detected in the Damara and Dorper sheep. This is most likely a consequence of recent intermixing between indigenous and commercial breeds. The levels of genetic diversity within individual types were generally lower compared to commercial breeds. This study revealed an understanding regarding genetic variation within and among indigenous sheep breeds, which can be used as baseline information for establishing conservation and breeding programmes
Africa: sequence 100,000 species to safeguard biodiversity
Build a major genomics resource on the continent to help breeders and conservationists