Factors affecting the adoption of selected wheat (triticum aestivum) production technologies by farmers in Noro and Rongai Divisions of Nakuru Districts, Kenya

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Date
2001
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Alice Chesambu Ndiema
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Despite the of continuous generation of new technologies from agricultural research stations for wheat production among the farming communities in Kenya, adoption of the same has been very low. Wheat is usually grown for commercial purpose and its production over the years has not been able to meet the country's demand. The shortfall is supplemented by imports that can be reduced by farmers ' adoption of recommended wheat production technologies. The purpose of this study was to identify and describe factors affecting the adoption of selected wheat production technologies by farmers in Njoro and Rongai divisions ofNakuru district. The factors, derived from previous studies and observations, included high yielding varieties, land preparation, fertilizer application, improved seed, pests, disease and weed control. The design was Ex-Post facto with a population of (273) wheat farmers. A sample size of (150) wheat farmers was selected from Njoro and Rongai divisions using stratified proportional random sampling technique. The data was collected using a validated questionnaire and analysed using descriptive and inferential statistics. Hypotheses were tested using Chi-square at a=0.05. Based on the results, it is concluded that an average level of20.02% adoption of wheat production technologies was very low with 10% of the sampled farmers having adopted the use of high yielding varieties. The most constraining factors were delay in distribution (98.7%), and inaccessibility of the technologies (96.7%). Access to credit was possible only to 7.3% of the farmers. The Common source of information to the wheat farmers was their fellow farmer (52%) and only 38.7% could purchase seed from authorised dealers, on perception, the farmers in Rongai had a higher mean score (1.90%) than that of Njoro mean (1.68%). Chi-square test showed that independent variables, namely, education level, kind of land ownership and farm size significantly affected
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