The agricultural sector is at a critical crossroads, facing an array of challenges driven by climate change, resource depletion, and the need for sustainable practices. In the Netherlands, a leading global exporter in the agrifood industry, the transition towards sustainable agriculture is both urgent and complex. This study investigates the role of Knowledge-Intensive Intermediaries (KIIs), particularly innovation consultancies, in facilitating this transition. KIIs act as mediators who engage with a diverse range of clients and understand their varying needs, they possess a deep understanding of the sector and the differing values of its stakeholders. This unique position enables them to effectively drive technological adoption, foster collaboration, and address systemic barriers within the agricultural sector. Through a qualitative case study approach involving in-depth interviews with farmers, agricultural IT firms, policymakers, and academic representatives, this research provides a comprehensive analysis of how KIIs, such as Epiic, support the implementation of sustainable agricultural technologies and assist in innovation adoption.

The findings reveal that while technological advancements such as precision farming and digital agriculture offer promising solutions for enhancing productivity and sustainability, the adoption of these innovations is hindered by a range of factors. These include financial constraints, knowledge gaps, regulatory challenges, and socio-economic disparities among farmers. KIIs play a crucial role in mitigating these challenges by acting as knowledge brokers, boundary spanners, and systemic innovators. They facilitate knowledge transfer, provide strategic consultancy, and create collaborative networks that bridge the divide between various stakeholders, including farmers, researchers, and policymakers. Furthermore, KIIs contribute to fostering an inclusive transition by addressing the economic and social dimensions of sustainability, ensuring that small-scale farmers and marginalized groups are supported in adopting new technologies without facing disproportionate burdens.

This study contributes to the literature by highlighting the multifaceted roles of KIIs in the agricultural innovation ecosystem, particularly within a multi-stakeholder environment. It demonstrates their potential impact on both the technological and social aspects of sustainability, showcasing how their deep understanding of diverse stakeholder needs enables them to drive innovation and collaboration effectively. It underscores the importance of a holistic approach to agricultural reform, one that integrates technological innovation with socio-economic and environmental considerations. The research offers valuable insights for policymakers, practitioners, and researchers on the strategic roles that KIIs can play in promoting sustainable agricultural practices. Ultimately, the study proposes a framework for understanding how KIIs can drive systemic change in agriculture, paving the way for a more resilient and sustainable future for the sector in the Netherlands and beyond

Artificial Intelligence (AI) and Machine learning (ML) applications are being widely used to solve different problems in different sectors. These applications have enabled the human-effort and involvement to be very low. The AI/ML systems
make their own predictions and do not require a great deal of human help. However, over the last few years several incidents of the developed systems
have led to questions regarding the transparency of those AI/ML systems. Without expertise, it is not always as straightforward to understand
certain predictions. This pressing issue has led to the emerging topic of Explainable Artificial Intelligence (XAI). In this research, we will present the current work on a specific type of XAI, namely model-specific XAI. Model-specific XAI techniques are particular to certain types of ML techniques. We will look into several recent model-specific XAI techniques and provide the advantages
and disadvantages. Within similarities we find that there is a set of general requirements that the techniques should adhere to (expertise, bias, time, privacy
and performance). We characterize the techniques in feature-based, concept-based and logic-based. With regard to future work, there is room for
improvement on several areas. For example, this includes work from exploring hybrid techniques to investigating how current techniques can improve
the privacy.