The impact of farmers research network project on diversified legumes among smallholder farmers in Singida region, Tanzania

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Date

2024

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Volume Title

Publisher

Sokoine University of Agriculture

Abstract

Legume production in Singida Region has been conducted for years but with low yield production. The study aimed at assessing the impact of Farmers Research Network on diversified legumes among smallholder farmers in Singida Region. The study employed cross-section research design with 204 respondents using questionnaire survey and key informant interviews. A two-stage Heckman selection model was used to determine factors influencing the adoption of diversified legume production and farmers perception was assessed using Likert scale and the analysis was then subjected to principal component analysis for factor analysis. Descriptive and content analysis were conducted for quantitative and qualitative data respectively employing Microsoft excel for results presentation. The results on the factors influencing adoption showed that age, education, farm size, major occupation, experience, land size, access to credit, and motivation were significantly associated with adoption of diversified legume production. The adoption of varied legume production in the research area was found to be statistically significantly influenced by the farmers' age, education level, farm size, main occupation, experience, land size, availability to credit, and motivation. Factors such as ease of cultivation, fair distribution of seeds, food availability, increase in income level, high legume harvest, and higher profitability of legumes (p < 0.05) significantly influenced smallholder farmers' awareness and perception of diversified legumes. Therefore, Farmers Research Network project has positively impacted diversified legumes on smallholders farmers in Singida Region. There is a need to increase training sessions to create awareness on diversified legumes.

Description

Dissertation

Keywords

Diversified legumes, Smallholder farmers, Two Stage Heckman Selection Model, Principal Component Analysis

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