Abstract

Transformative rural smallholder agriculture addressing biophysical constraints requires farmer-led innovations for increased technology adoption. Following the need to further intensify the cereal push-pull technology (PPT) for pest and weed control through integration with vegetables, we conducted surveys to determine suitable vegetables across three different counties in Kenya namely Homabay, Siaya, and Trans-Nzoia. Farming in these areas is predominantly maize based and introducing vegetables to the system will improve household food and nutritional security, together with income. A systematic random sampling method was used to sample a total of 124 smallholder farmers who were interviewed during field days where farmer awareness of technology was mobilised. Descriptive results showed kale (47%) and black nightshade (30%) were the most preferred high-value vegetables for integration into push-pull plots in these regions. This was followed by cowpea (15%), onion (6%) and tomato (2%) underlying the wide range of farmer preferences and priorities. There were also gender differences in vegetable preferences with men preferring capital intensive and high value vegetables, while women preferred traditional vegetables. Results from multinomial logit model revealed that age of the farmer, education level, gender, and farming experience were determinants of smallholder farmers’ vegetable preferences for integration into the PPT plots. The contribution of these determinants to farmer preferences varied across different vegetables where the contribution of the market value of the vegetable in selection diminished with while the effect of farmers’ age. Overall, male farmers preferred capital-intensive and market-oriented vegetables in compared to their female counterparts. We conclude that gender and geographic location play a pivotal role in farmers` crop selection for integration in the PPT. Targeted awareness creation pathways accounting for these factors will be critical for wider adoption of the PPT. Key words: Food security, Multinomial Logistic Regression Model, Participatory rural appraisal, Sustainable agricultural intensification, Technology adoption

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