The generation interval and season of birth do not affect age at first calving, birth weight and calving interval of Mozambican Angoni cattle.

The Angoni cattle breed's contribution to the country's economy is crucial, as it significantly contributes to animal draught power and meat supply, despite not being primarily used for milk production. Despite its importance, there is a lack of comprehensive research conducted to characterize this breed. This study aimed to investigate the impact of the generation interval (GI) and season of birth (SB) on key reproductive parameters, including age at first calving (AFC), birth weight (BW), and calving interval (CI) in angoni cattle. Data sourced from the Angónia Research Station (ARS) included records for 425 heifers' AFC, 1684 calves' BW, and 1272 cows' CI. The study calculated overall averages and explored the relationships between generation intervals, the season of birth, and the aforementioned reproductive traits. The mean values for AFC, BW, and CI were determined as 1475.40 days, 18.49 kg, and 634.62 days, respectively. The analysis revealed that both generation interval and season of birth exhibited weak relationships, and their influence did not yield significant effects on the reproductive traits under investigation (P > 0.05). The observed variability ranged from 0.37 to 0.46% for AFC, 0.10-0.01% for BW, and 0.11-0.26% for CI. In conclusion, this study determined that neither generation interval nor birth season significantly affected the age at first calving, birth weight, or calving interval in Angoni cattle.

Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network-Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes.

Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day's images, or even an entire sampling trip's images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection.

Genetic diversity and relationships among three Southern African Nguni cattle populations.

The Nguni cattle breed has distinct populations that are adapted to the different ecological zones of Southern Africa. This study was carried out to assess genetic diversity and establish the relationships among South African (SA), Mozambican (Landim), and Swazi Nguni cattle populations, using 25 microsatellite markers. Genotypic data were generated from deoxyribonucleic acid (DNA) samples of 90 unrelated individuals of the three cattle populations, collected from government conservations and stud herds. DNA profiles of five local beef breeds were used as the reference populations. Most of the 25 microsatellite markers were highly polymorphic across the studied populations, with an overall polymorphic information content (PIC) mean of 0.676. Genetic diversity within populations was high with expected heterozygosity varying from 0.705 ± 0.024 (Landim) to 0.748 ± 0.021 (SA Nguni) and mean number of alleles being highest in the SA Nguni (7.52 ± 0.42). Average observed heterozygosity (0.597 ± 0.046) compared to the expected heterozygosity (0.719 ± 0.022) was lowest for the Swazi Nguni, which also had a high number of Hardy-Weinberg Equilibrium (HWE) deviated loci (13), confirming the relatively high level of inbreeding (0.158 ± 0.058) in that population. Analysis of molecular variance revealed only 9.61% of the total variation between the populations and 90.39% within populations. A short genetic distance (0.299) was observed between Landim and Swazi Nguni, with the SA Nguni (> 0.500) being the most genetically distant population. The distant relationship between SA Nguni and the other two Nguni cattle populations was further confirmed by a principal coordinates analysis. The three Nguni populations clustered independently from each other, despite some evidence of admixture. Therefore, it can be concluded that SA Nguni, Landim, and Swazi Nguni populations in Southern Africa exhibit high levels of genetic diversity and are genetically distant; with the two latter populations being less genetically apart. These results present useful information for the development of strategies for regional management of animal genetic resources, through conservation and sustainable utilisation.