Determining the pregnancy rate of artificial insemination and the related factors affecting the pregnancy rate in cattle in the Tanga district, Tanzania.

Abstract

In the Tanga region in Tanzania many farmers rely (partly) upon their dairy cattle as a source of income. Therefore, breeding management is of utmost importance. Cows are polyoestrous and have oestrous cycles throughout the year. Several methods have been developed in order to predict the right moment for breeding. However, some may require extensive access to technology and financial resources. Thus, visual observation of heat is the only option. This is a time consuming and sometimes difficult option, as not all cows show clear signs of heat such as standing heat. In this study a questionnaire was composed in order to answer the question if the adoption of artificial insemination (AI) has a positive influence on the pregnancy rate. 99 farmers from Tanga city council were interviewed about adoption of AI, pregnancy rate, number of inseminations needed for pregnancy and some general factors. However, only 45 interviews were included in the final dataset as a result of excluded non-applicable data points in various variables and interviews. Using a generalized mixed model, the association between pregnancy rate and breeding system, grazing system and highest education level were tested. Farmers who used AI as a breeding system had a negative association with pregnancy rate (p<0.05). Furthermore, all education levels showed significance. In addition, a system to assess the heat detection quality (HDQ) was made. This scoring system included factors such as oestrous behaviour watched for by the farmer, moment and duration of observations, calendar use and doing other work while watching the cows for heat. To the authors knowledge no system to assess HDQ existed before this study. This system can be used by AI technicians and farmers to improve their heat detection quality.