ABSTRACT
BACKGROUND: Brucellosis is a bacterial zoonosis of public health and economic importance worldwide. It affects a number of domestic animals, wild animals and humans. Human brucellosis originates from either livestock or wildlife. The species of Brucella circulating in wild animals in Tanzania is largely unknown due to insufficient surveillance. This study was carried out to identify Brucella species found in selected wildlife hosts in the Serengeti ecosystem. METHODOLOGY: The study used a total of 189 archived samples that were obtained from cross-sectional studies previously conducted between 2000 and 2017 in the Serengeti ecosystem in Tanzania. Whole blood, serum and amniotic fluid collected from buffalos, lions, wildebeest, impala, zebra and hyena were available for DNA extraction. Multiplex polymerase chain reaction for B. abortus, B. melitensis, B. ovis and B. suis (AMOS PCR) and quantitative real-time PCR (qPCR) targeting the bcsp31 and IS711 genes for Brucella genus detection and the IS711 targets alkB for B. abortus and BMEI1162 for B. melitensis were used to detect Brucella strains. RESULTS: Out of the 189 samples tested, 12 (6.35 %) and 22 (11.6 %) were positive to AMOS-PCR and qPCR, respectively. Most of the positive samples were from lions (52.6 %) and buffaloes (19.6 %). Other animals that were positive included: wildebeest (13.6 %), impala (13.6 %), zebra (4.5 %) and hyena (4.5 %). Out of 22 positive samples, 16 (66.7 %) were identified as B. abortus and the other six samples did not amplify for neither B. abortus nor B. melitensis. CONCLUSIONS: The detection of Brucella DNA in archived wild animal samples shows testing potential of samples collected from this population. The zoonotic species B. abortus and B. melitensis detected in wild animals have previously been reported in livestock and humans in the region. The findings suggest that, due to the contact network, some of the identified wild animal hosts in this study could be reservoirs for infections in domestic animals and humans within the Serengeti ecosystem while others are likely dead-end hosts. One Health control strategies and continuous surveillance programs in other wildlife reserved areas should be implemented to help predicting transmission in livestock and humans in the region.
ABSTRACT
Poultry production is an important way of enhancing the livelihoods of rural populations, especially in low- and middle-income countries (LMICs). As poultry production in LMICs remains dominated by backyard systems with low inputs and low outputs, considerable yield gaps exist. Intensification can increase poultry productivity, production and income. This process is relatively recent in LMICs compared to high-income countries. The management practices and the constraints faced by smallholders trying to scale-up their production, in the early stages of intensification, are poorly understood and described. We thus investigated the features of the small-scale commercial chicken sector in a rural area distant from major production centres. We surveyed 111 commercial chicken farms in Kenya in 2016. We targeted farms that sell the majority of their production, owning at least 50 chickens, partly or wholly confined and provided with feeds. We developed a typology of semi-intensive farms. Farms were found mainly to raise dual-purpose chickens of local and improved breeds, in association with crops and were not specialized in any single product or market. We identified four types of semi-intensive farms that were characterized based on two groups of variables related to intensification and accessibility: (i) remote, small-scale old farms, with small flocks, growing a lot of their own feed; (ii) medium-scale, old farms with a larger flock and well located in relation to markets and (iii) large-scale recently established farms, with large flocks, (iii-a) well located and buying chicks from third-party providers and (iii-b) remotely located and hatching their own chicks. The semi-intensive farms we surveyed were highly heterogeneous in terms of size, age, accessibility, management, opportunities and challenges. Farm location affects market access and influences the opportunities available to farmers, resulting in further diversity in farm profiles. The future of these semi-intensive farms could be compromised by several factors, including the competition with large-scale intensive farmers and with importations. Our study suggests that intensification trajectories in rural areas of LMICs are potentially complex, diverse and non-linear. A better understanding of intensification trajectories should, however, be based on longitudinal data. This could, in turn, help designing interventions to support small-scale farmers.
