Ticks and tick-borne diseases are serious constraints to livestock production in Tanzania and other sub-Saharan African countries. Despite this, knowledge on the abundance of tick species infesting cattle in most parts of Tanzania is insufficient or lacking. This study was conducted to identify species and establish the abundance of ticks infesting cattle in Mara, Singida and Mbeya regions of Tanzania. The ticks were collected from one side of the body, counted and identified, based on morphological characteristics; to species level. The mean tick count per animal was significantly higher in Mara (35.8±4.3, p=0.0001) as compared to Singida (12.9±2.1) and Mbeya (7.0±0.4) regions. Young animals in Mara (24.7±6.0, p=0.0395) and Mbeya (5.4±0.3, p=0.0252) exhibited relatively lower mean tick counts compared to the weaners (Mara = 33.8±6.5, Mbeya = 7.2±0.7) and adult animals (Mara = 46.3±8.4, Mbeya = 7.8±0.7). Seven tick species from three different genera, namely Ambylomma, Hyalomma, Rhipicephalus (including the subgenus Boophilus), were identified. However, only five species (A. lepidum, A. variegatum, R. decoloratus, R. microplus and H. rufipes) were observed in all the three regions. R. appendiculatus and R. evertsi were not found in Mbeya and Mara respectively. The most prevalent species in Mara, Singida and Mbeya were R. appendiculatus (50.5%), A. lepidum (31.2%) and R. evertsi (35.6%), respectively. This study showed the existence of a variety of tick species, most of them being of veterinary importance. Therefore, strategic planning and cost-effective tick control measures should be implemented in order to reduce losses caused by ticks and tick borne diseases in the study area.
Key words: Ixodid ticks, abundance, distribution, cattle, Tanzania
AAP, Aggregation-attachment pheromones; ACP, Africa Caribbean and Pacific; ANOVA, analysis of variance; BCS, Body condition score; CI, Confidence interval; ECF, East Coast fever; EPINAV, Enhancing Pro-poor Innovation in Natural Resources and Agricultural Value Chains; FAO, Food and Agriculture Organization; GDP, gross domestic product; GLM, General linear model; MLFD, Ministry of Livestock and Fisheries Development; MRSP, Mbeya Region Socio-Economic Profile; NORAD, Norwegian Agency for International Development; SAS, statistical analysis system; SE, standard error; SUA, Sokoine University of Agriculture; TBDs, tick borne diseases; UNZA, University of Zambia; USD, United States dollar; MRUF, Mara Region-The Unique Features; SRSP, Singida Region Socio-Economic Profile; NBS, National Bureau of Statistics.
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