A study was conducted using maize samples collected from different agroecological zones of Kenya (n = 471) and Tanzania (n = 100) during the 2013 maize harvest season to estimate a relationship between aflatoxin B1 concentration and occurrence with weather conditions during the growing season. The toxins were analysed by the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. Aflatoxin B1 incidence ranged between 0–100% of samples in different regions with an average value of 29.4% and aflatoxin concentrations of up to 6075 µg/kg recorded in one sample. Several regression techniques were explored. Random forests achieved the highest overall accuracy of 80%, while the accuracy of a logistic regression model was 65%. Low rainfall occurring during the early stage of the maize plant maturing combined with high temperatures leading up to full maturity provide warning signs of aflatoxin contamination. Risk maps for the two countries for the 2013 season were generated using both random forests and logistic regression models.
A lot of public funds have been used for investment in wheat research in Kenya. The concern is whether it is worthwhile to continue investing. The problem is there is no information on what returns to investments have been achieved to guide the continued allocation of resources to wheat research. To address this problem, this paper seeks to estimate returns to wheat research investments in Kenya, and the wheat varietal adoption and turnover rates that influence it. The Benefit-Cost Analysis (BCA) model was applied to estimate the returns to wheat research, in terms of three indicators: Benefit-Cost Ratio (BCR), Net Present Value (NPV), and Internal Rate of Return (IRR). Adoption index was used to estimate wheat varietal adoption rates (VAR). The area-weighted average variety age (WAVA) was used to estimate the varietal turnover rate (VTR). The Five-Point Likert scale model was used in assessing production risks. A field survey was used for data collection in selected wheat-producing Counties of Kenya. The results generated were a BCR of 1.47, an NPV of 23.31 million Kenya Shillings, an IRR of 41%. The VAR was 42% and VTR was 15.65 years. The main wheat production risks were output price fluctuations, seed availability, pests, and diseases in that order of ranking. In conclusion, return on investments in wheat research is positive, though relatively low compared to other countries, largely due to low varietal adoption and turnover rates and prevalence of high production risks. The recommendation is that to improve returns to wheat research in Kenya, varietal adoption and turnover rates should be improved and production risks should be minimized or eliminated.
This study examines the factors that influence the adoption of new improved wheat varieties (NIWV) by wheat farmers in Nakuru and Narok counties in Kenya. Cross-sectional data from 344 randomly selected wheat farmers from the Njoro and Rongai sub-Counties in Nakuru County; and Narok South and Narok North sub-counties in Narok County, Kenya were investigated. Probit model was run to estimate the factors influencing the adoption rate of improved new wheat varieties. Results derived from model estimates indicate that farmers' adoption of improved wheat varieties in the study area is positive due to education, availability of information, off-farm income, distance to inputs and produce markets, and exposure to extension advice services and access to credits. The study recommends that the public and private sectors promote access to advisory services to improve the dissemination of certified wheat seeds to farmers through training, workshops, and seminars.
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