Knowledge, Innovation and Technology in Scientific Research Vol. 5

Agricultural knowledge delivery is changing from the electronic distribution of recommendations towards systems that interpret observations, retrieve evidence and generate interactive advice. This transition is frequently presented as a technological progression, although the evidence supporting digital communication, predictive models and generative advisory systems concerns different outcomes and levels of validation. This critical narrative review examines that distinction through literature on agricultural extension, development economics, machine learning, knowledge systems and responsible innovation, covering publications available up to 27 July 2026. Targeted scholarly searching and reference verification informed a thematic, design-sensitive synthesis rather than a pooled estimate of effectiveness. Experimental evidence indicates that digital information can improve selected knowledge, adoption and production outcomes, but null findings and substantial contextual variation challenge claims of universal benefit. Predictive accuracy does not establish the value of a recommended action, while language-model benchmarks and expert assessments remain insufficient substitutes for prospective evidence on farm decisions and welfare. Recent generative artificial intelligence studies demonstrate useful capabilities alongside sensitivity to evaluation task, workflow and outcome definition. Across these literatures, information relevance, farmer resources, intermediary support, commercial incentives and control over data shape whether technical capability becomes practical benefit. The review develops a conditional-capability framework in which an advisory system must establish an adequate decision context, use traceable evidence, communicate uncertainty, support feasible action and retain accountable routes for correction. This framework is a synthesis-derived proposition, not a validated maturity scale. Research priorities include comparative field evaluations, multilingual and out-of-distribution testing, measurement of human oversight costs, gender-sensitive outcome assessment and longitudinal institutional analysis. Intelligent advisory should therefore be understood as a property of an accountable knowledge service rather than of a model alone. Its contribution will depend on strengthening, rather than bypassing, the social and scientific relationships through which agricultural knowledge becomes usable.