Influence of Farmers’ Socio-economic Characteristics on Adoption of Bambara Nut Production in Western Kenya

A survey was conducted to determine the effect of the socio-economic and institutional factors on farmers’ adoption of Bambara nut as food security crop. Purposive survey research design was used to generate both qualitative and quantitative data. 384 respondents were interviewed in the study. Proportionate sampling technique was used to select 131, respondents from Kakamega North Sub- county, 127 from Butere and 94 from Matungu and 32 respondents from Mumias subcounties based on population. Primary data was collected through structured questionnaire, interview schedules alongside focused group discussion. Findings indicated that that seven variables were statistically significant and contributed to adoption. These factors include: subcounty of farmer’s residence, gender, farm size, on-farm income, labor, member of social group, marketing problems, access to extension services and respondents’ access to credit. Four other factors (age, level of education marketing and credit) were not significant. Chi-square test showed that the estimated model fitted the data reasonably well and indeed the variables were jointly significant in explaining adoption of Bambara production and utilization in Kakamega County. In order for smallholder farmers to benefit from neglected food crops there is need for stakeholders to contribute towards formulating relevant policies and impliment research programmes that will promote and commercialize amongst others Bambara amongst smallholder farmers. This would lead to increased smallscale growing of Bambara nut and thus contribute to ensuring food security at household level among resource poor farmerss in Kakamega Count of Kenya

Genetic Diversity of Dry Bean (Phaseolus vulgaris L.) Accessions of Kenya Using SSR Markers

Aims: To determine the genetic diversity existing within the Kenyan dry bean using SSR markers. Place and Duration of Study: This study was conducted in Western Kenya and Bangor University, North Wales, between September 2010 and December 2012.Methodology: Thirty five (35) marketable dry bean samples collected from farmers, market centers as well as seed stockists were subjected to SSR analysis. Data generated was subjected to analysis with the GenAlEx 6.4 software assuming Hardy-Weinberg equilibrium to determine gene diversity index, number of polymorphic loci and alleles, genetic distances, analysis of molecular Original Research Article Maryrose et al.; AJEA, 5(4): 306-319, 2015; Article no.AJEA.2015.030 307 variance (AMOVA) and principal components analysis (PCA). NYTS-pc 2.1 software was used to construct an unweighted pair group method arithmetic averages (UPGMA) dendogram using the generated similarity coefficients. Results: Of the 7 SSR primers tested, 5 SSR primers were found to be polymorphic and used to screen the bean samples. The 5 primer combinations generated 49 polymorphic bands in 35 samples. Analysis of molecular variance accredited 8% of the disparity to diversity among the populations while the majority of the diversity (92%), resided within populations. The gene diversity index ranged from 0.1267 in the market population to 0.2377 in the Western province population. The highlands of Eastern province had a gene diversity index of 0.1475 while the dry lands had 0.1991. Cluster analysis segregated the bean samples into 9 clusters. Conclusion: There exists considerable variation in the dry bean of Kenya that is narrowing. There is need to intensify efforts to broaden the bean variation for sustainability. The population genetics of dry beans of Kenya are a possible guide to future bean breeding and germplasm management in Kenya. Keywords: SSRs; Phaseolus vulgaris; dry bean; germplasm characterization; and genetic variatio

Micro-Dosing of Lime, Phosphorus and Nitrogen Fertilizers Effect on Maize Performance on an Acid Soil in Kenya

High cost of inorganic fertilizers and lime has precluded their use by smallholder farmers to remedy the problem of soil acidity and infertility in Kenya. To address the problem, we tested a precision technique referred to as micro-dosing, which involves application of small, affordable quantities of inorganic inputs on an acid soil in Busia County, Kenya. Experimental treatments were N-fertilizer (0 and 37.5 kg N ha-1), P-fertilizer (0 and 13 kg P ha-1) and lime (0, 0.77 and 1.55 tons lime ha-1). 37.5 kg N and 13 kg P ha-1 are 50% of the recommended fertilizer rates for maize production in Kenya while 0.77 and 1.55 tons lime ha-1 are 25 and 50% of the actual requirement. Soil chemical changes, maize grain yield and nutrient recovery were determined. Lime and P-fertilizer significantly affected only the top-soil pH, Ca, Mg and available P, while the effects of N-fertilizer were evident on both top- and sub-soil N likely due to its faster mobility than P and lime. Grain P-fertilizer recovery efficiencies were 14 and 16-27% due to 13 kg P and 13 kg P + 0.77-1.55 tons lime ha-1, respectively. N-fertilizer recovery efficiencies were 37 and 42-45% due to 37.5 kg N and 37.5 kg N + 0.77-1.55 tons lime ha-1, respectively. Fertilizers applied to supply 37.5 kg N, 13 kg P and 0.77-1.55 tons lime ha-1 increased grain yield above the control by 134, 39 and 12-22%, respectively, therefore micro-dosing of these inputs can increase maize production on Kenyan acid soils