Regulatory frameworks shape how agricultural sectors adapt to climate change, influencing both transitional practices and transformational change. This study examines how different actors - government agencies, farm advisory bodies, research institutions, indigenous Māori, and the media - frame climate adaptation, transition, and transformation through public discourse. Using Natural Language Processing (NLP) and machine learning techniques, including topic modelling, sentiment analysis, and collocation networks, we conduct a comparative linguistic analysis of five purpose-built corpora from Aotearoa New Zealand. Our findings reveal how different narratives and signals influence risk perception, compliance, and adaptive decision-making in agriculture. By identifying shared concerns and discursive misalignments, we highlight opportunities to enhance communication strategies and improve governance effectiveness in climate adaptation. This study contributes to the understanding of regulation as a communicative process and demonstrates the potential of NLP for evidence-based regulatory analysis in environmental governance.