The ability of farmers to acquire knowledge to make decisions is limited by the information quality and applicability. Inconsistencies in information delivery and standards for the integration of information also limit decision making processes. This research uses a similar approach to the Knowledge Discovery in Databases (KDD) methodology to develop an ICT based framework which can be used to facilitate the acquisition of knowledge for farmers’ decision making processes. This is one of the leading areas of research and development for information technology in an agricultural industry, which is yet to utilize such technologies fully. The Farmer Knowledge and Decision Support Framework (FKDSF) takes information provided to farmers and utilizes processes that deliver this critical information for knowledge acquisition. The framework comprises data capture, analysis, and data processing, which precede the delivery of the integrated information for the farmer. With information collected, captured, and validated from disparate sources, according to defined sets of rules, data mining tools are then used to process and integrate the data into a format that contributes to the knowledge base used by the farmer and the agricultural industry.
Agricultural Information Dissemination
The dissemination, “to spread or give out something, especially news, information, ideas, etc., to a lot of people” (Cambridge University Press, 2009), or delivery, “the carrying and turning over of letters, goods, etc., to a designated recipient or recipients” (Dictionary.com, LLC, 2009), of agricultural information is integral to facilitating the adoption of new farm technologies. The farmer needs to be aware of the best practices and advances in farm management and breeding. Information on general agronomy practices such as seeding, fertilizer, pest management, harvesting and marketing provides the knowledge base for farmers to make informed decisions. This is supported by Umber (2006) who reported that, for information to be used effectively by growers, it needs to be delivered in a format that can be easily integrated into grower decision making. However, this may only be effective if farmers have the skills to interpret this data and to make decisions relevant to their individual situations (Armstrong et al., 2007).