Influence of Selected Socio-Economic Factors on Crop Productivity under Irrigated Agriculture among Kimira-Oluch Smallholder Farmers of Homa Bay County, Kenya

Abstract

Irrigation technology plays a critical role in enhancing agricultural productivity globally. For the last ten years, the availability of water for irrigation has been rapidly declining yet the demand for water for irrigation has been on arise. The introduction of irrigation technologies as immediate interventions for crop productivity has proved to be effective in many countries and most likely has changed the phenomenon to a more reliable and sustainable agricultural production. Despite many efforts by the Kenyan government to improve the country’s agricultural sector, still there is low crop productivity due to the continued practice of rainfed agriculture. The main objective of this study was to determine the influence of selected socio-economic factors that is the attitude towards the introduction of irrigation intervention, Irrigation technologies, Gender participation, Age of the participant, Household income, and Level of education on crop productivity among Kimira-Oluch smallholder farmers in Homa Bay County, Kenya. A survey methodology with an Ex-post facto research design was used with a sampling frame consisting of 340 smallholders. Data were collected through interview schedules administered to participants in irrigated agriculture and Non-participants (rainfed agriculture). Inferential statistics used involves multiple regression analysis and two-way ANOVA. The hypothesis was tested at a 5% level of significance and the results revealed an absolute value of correlation coefficient of 0.823 which indicates that there is a high correlation between the crop productivity and selected socio-economic factors that is (ATT, IRT, GP, HI, AR and ELR). The findings further revealed that R2= 0.714 of the proportionate variation of the model an indication that, the selected socio-economic factors can predict 71.4% of crop productivity and the other variables not included in the model can only predict 29.6% of productivity. By using analysis of variance (ANOVA) the findings revealed that the regression model predicts that there is a significant improvement in crop productivity and the farming system as well. This was revealed by the F-ratio of 71.83 indicating a significant relationship at a p-value of= 0 .000. The model will be of greater use in determining the influence of socio-economic factors on crop productivity in other regions without necessary undergoing the full research processes thus reducing the research expenses by almost half.