Agriculture-livestock production plays a vital role in poverty reduction and directly impacts on the growth of developing economies [26]. This impact can only be measured when enough data on agriculture and livestock is made available and appropriate planning done. A study by Bonnet et al. [27], reveal that many livestock sector actors hardly share data owing to data accessibility challenges arising from data fragmentation, lack of standardization, and most importantly lack sharable virtual webspace. Additionally, the available datasets is held by due to challenges were not sharing enough data or information, because of accessibility and sharing problems, fragmentation of datasets lying under the responsibility of too many stakeholders, lack of standardization of contents, lack of a sharable virtual web space or due to sociological and institutional barriers.
Recent plans to implement Livestock Identification and Traceability System (LITS) in Kenya aim at adoption of innovative technology to manage the process of identification, recording and managing traceability data more effectively [28]. Adoption of such data revolution requires the development of a vibrant ecosystem that actively engages in closing data gaps as well as building capacity of actors both in the private and public sector involved in managing livestock data [12]. In 2017, a Ministerial Conference on Global Open Data for Agriculture and Nutrition was held in Nairobi which revealed that Kenya had increasingly invested in data collection to help in the growth of Agriculture. The collaboration of the National Ministry responsible for livestock matters with the County Governments and other development partners were aimed at putting in place an agricultural growth and transformation strategies. These strategies aim at contributing to the general availability of data for decision-making in agriculture and nutrition, thereby making agriculture more precise and profitable for all by providing information to all players along the entire agricultural value chain [12]. The livestock recording schemes collect livestock data that rests within their databases with no strategic mechanisms of transferring these data to benefit other livestock industry actors. Decentralized models and BT can leverage on LDM challenges by utilization of electronic livestock records to ensure provenance, trust, security, traceability and enhanced livestock data sharing.