Effects Of Training Farmers On Integrated Soil Fertility Management Technologies On Crop Yield And Income In Mbale Division, Kenya

Abstract

The agro-climatic conditions in western Kenya present the region as a food surplus area yet people are still reliant on food imports. Numerous interventions through Integrated Soil Fertility Management (ISFM) practices to this front have not yielded the expected results. With this background the study sought to: (i) identify the contribution of ISFM interventions to farm productivity, (ii) to identify gaps in knowledge translation and (iii) to establish the major factors determining uptake of ISFM technologies among smallholder farmers. The study was conducted in sub-locations in Mbale division, Vihiga County. Purposive sampling was used in selecting 80 farmers, half of whom were trained in ISFM technologies while the other half was not trained. Questionnaires were used to collect data from the 80 farmers in the study area. Data was analyzed using descriptive statistics. Cross tabulation was used for examining the relationship between categorical (nominal or ordinal) variables, and the bivariate correlations procedure was used to compute the pair wise associations between scale or ordinal variables. A Spearman's rank-order correlation was run to determine the relationship between the respondents and the constraints in implementing ISSF technologies. Probit regression was used to predict the socio-economic factors influencing the uptake of ISFM technologies among smallholder farmers. Maize, the main staple crop, had an average acreage of 0.55ha among the trained while non-trained had an average acreage of 0.53ha with the difference being statistically insignificant (p=0.678). Maize recorded an average of 3.6 t/ha among the trained and 3 t/ha among non-trained farmers (p=0.528). Trained farmers registered an annual mean household income from farming activities of USD 3,587 which was not significantly different from that of non-trained who had USD 2,957 (p=0.320). The farm input largely used was organic manure (mean of 234 kg/ha) and the amount used did not have a statistically significant difference between the two study groups at an average of 241 kg/ha for the trained and 227 kg/ha for non-trained (p=0.673). Probit regression model identified age of the household head (r=0.038), perception of the extent of soil depletion (r=0.291) and off-farm income (r=0.597) as possible predictor factors likely to influence the farmers’ use of inorganic fertilizers at p=0.045. On the use of manure, labour (r=0.678), and group membership (r=0.480) were identified as possible predictor factors likely to influence the farmers’ use of manure at p=0.0213. With regard to the use of compost manure, probit regression identified education level of the household head (r=-0.059), ratio of farm worker to household size (r=0.332) and extension assistance (r=0.039) as possible predictor factors likely to influence farmers’ usage of compost manure at p=0.018. The findings show that proponents of ISFM should consider the age, education level and financial capacity of farmers when promoting ISFM technologies. The findings will help farmers, extension officers, researchers and donors in identifying entry points that can help in the development of innovative ISFM technologies that are region specific.