Key research themes
1. How do Agricultural Knowledge and Innovation Systems (AKIS) facilitate effective knowledge sharing and empowerment among smallholder and new small-scale farmers?
This research theme investigates the structure, function, and dynamics of Agricultural Knowledge and Innovation Systems (AKIS) with a focus on how these systems support knowledge dissemination, innovation, and empowerment among smallholder and new small-scale farmers. Recognizing farmers as active participants rather than passive recipients, the studies explore actor linkages, social networks, advisory service effectiveness, and the role of opinion leaders in influencing agricultural practices and food security. Understanding these interactions is crucial for designing advisory services and knowledge flows that improve decision-making, capacity building, and resilience at the farm level.
2. What are the challenges and pathways in enhancing Agricultural Innovation Systems (AIS) through integrated research, extension, training, and sustainability efforts?
This theme centers on understanding the complexities, obstacles, and solutions in operationalizing Agricultural Innovation Systems (AIS) by analyzing the subsystems of research and development (R&D), extension, training, and sustainability. It emphasizes systemic approaches, stakeholder coordination, capacity building, and policy frameworks essential to promote effective knowledge flows, sustainable innovations, and inclusive dialogues among actors. Research underscores the multifaceted nature of AIS and calls for integrated strategies to bridge gaps across institutional, technological, and socio-economic dimensions.
3. How can Semantic Web technologies and ontologies enhance agricultural knowledge representation, sharing, and intelligent decision support?
This research area explores the application of Semantic Web technologies, formal ontologies, and rule languages to model, represent, and share agricultural knowledge in a machine-interpretable manner. It addresses the challenges of knowledge heterogeneity, interoperability, and the need for inference capabilities to support decision-making and knowledge reuse. Advances include ontology development for underutilized crops, ontology evaluation frameworks, and the construction of linked open data in agriculture, contributing foundational infrastructure for intelligent agricultural information systems and global knowledge sharing.