International Review of Research in Open and Distributed Learning (Jul 2018)
Exploring Information Seeking Behavior of Farmers’ in Information Related to Climate Change Adaptation Through ICT (CHAI)
Abstract
In Tanzania, agriculture sector is known for employing more than 70% of the total population. Agriculture sector faces many challenges including climate change. Climate change causes low productivity in agriculture; low productivity is caused due to poor implementation of agricultural policies and strategies. This poor implementation of policies has also caused many farmers to be not competent in climate change adaptation. Over the years, provisions of agricultural advice and extension were provided by various approaches, including training and visit extension, participatory approaches, and farmers’ field schools. However, provision of agricultural advisory and extension service is inefficient. Also, in most cases the usage of most agricultural innovations and technologies developed is limited. A literature review indicates that the main reasons given by Tanzanian farmers for not using improved technology are not lack of knowledge or skill, but rather that the technologies do not contribute towards improvements (e.g., the technologies are not profitable or they imply to high risk). Thus, agricultural extension service needs to be geared towards teaching farmers how to develop innovative and cost effective technologies that are contextualized. Limited numbers of agricultural extension staff and less interactivity of Information and Communication Technologies (ICTs), such as radio and television, have been mentioned to be among the factors limiting the provision of agricultural advisory and extension services to the majority of farmers in Tanzania. The advancements in ICTs have brought new opportunities for enhancing access to agricultural advisory and extension service for climate change adaptation. In Tanzania, farmers and other actors access agricultural information from various sources such as agricultural extension workers and use of various databases from Internet Services Providers. Also there are different web – and mobile – based farmers’ advisory information systems to support conventional agricultural extension service. These systems are producing bulk amounts of data which makes it difficult for different stakeholders to make an informed decision after data analysis. This calls for the need to develop a tool for data visualization in order to understand hidden patterns from massive data. In this study, a semi-automated text classification was developed to determine the frequently asked keywords from a web and mobile based farmers’ advisory system called UshauriKilimo after being in use for more than 2 years by more than 700 farmers.
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