AI, Copyright, And Content Licensing In Digital Agriculture

24 January 2026, Africa: The large language models (LLMs) that power most artificial intelligence chatbots and other applications are exponentially more massive than those of earlier systems, relying on vast datasets and billions of parameters to produce their human-like outputs. Such scale is often celebrated as the driver of their cutting-edge capabilities. But scale alone does not guarantee sound results. The reliability (i.e., robustness, accuracy, and trustworthiness) of such systems significantly hinges on the quality and contextual relevance of LLM datasets. In short, not only quantity, but quality of available data for building gen AI models, is critical.

This is a major issue as AI systems expand their footprint across varied industries around the world—with particular implications for AI in agriculture, which has emerged as a priority sector, specifically for generative AI. For example, the African Union Continental AI Strategy, adopted in 2024, aims to leverage AI tools in pursuing its Agenda 2063 development plan and the Sustainable Development Goals (SDGs). Domestic policy documents in multiple African, South Asian, and Latin American countries attest to widespread commitments to use AI to speed agricultural modernization.

Also Read: John Deere Enters a New Era of Excavators

Global Agriculture is an independent international media platform covering agri-business, policy, technology, and sustainability. For editorial collaborations, thought leadership, and strategic communications, write to pr@global-agriculture.com