Farmers' Knowledge and Adoption Behaviour Towards Scientific Vegetable Cultivation Practices: A Review

Abstract

Scientific vegetable cultivation practices — including recommended varieties, soil testing, balanced fertilization, integrated pest management (IPM), good agricultural practices (GAP), safe pesticide handling and protected cultivation — have well-documented potential to raise yield, quality and farm income. Yet the productivity gains demonstrated on research stations depend on farmers first acquiring accurate technical knowledge and then translating that knowledge into sustained field practice, a transition that is frequently incomplete. This review synthesizes recent farm-level evidence on vegetable growers' knowledge and adoption behaviour across South Asia and East Africa. Across studies, average knowledge of recommended vegetable cultivation technologies reaches 74.72% among Tamil Nadu vegetable growers, yet overall adoption trails at 55.27%, with technically demanding, input-dependent practices such as soil testing, growth regulator use and foliar spraying adopted by only 40–48% of farmers despite comparable awareness. This knowledge–practice gap recurs across contexts: only 37% of vegetable growers in Lamjung, Nepal, met the threshold for integrated pest management adoption despite widespread awareness of pesticide-related risks; only 24.42% of Bangladeshi vegetable farmers reached the highest, most independent level of good-agricultural-practice application; and in Kenya, sufficient knowledge of agricultural biologicals did not reliably translate into their use. Training participation, extension contact, farming experience, education and access to complementary inputs and credit consistently emerge as the strongest predictors of translating knowledge into practice, while the gap is narrowest for low-cost, skill-based practices and widest for capital- or input-dependent technologies. The review concludes that closing the knowledge–practice gap in vegetable cultivation requires extension and training models that move beyond information delivery to build practical, hands-on competence, paired with reliable access to the inputs, credit and market linkages that input-dependent scientific practices require.