Treefera Launches Agricultural Risk Intelligence and Credit Risk Intelligence for Ag Lenders and Ag Tech Companies

**AI-Powered Agricultural Intelligence Set to Transform Zambian Farming Landscape** **LUSAKA, Zambia** – In a significant development for the global agricultural sector, Treefera, a London-based AI-native intelligence platform, has unveiled two groundbreaking products aimed at enhancing risk management for agribusinesses and lenders. The newly launched Agricultural Risk Intelligence and Agricultural Credit Risk Intelligence platforms promise to deliver unparalleled, real-time, field-level data, a technological leap that holds particular relevance for Zambia's climate-vulnerable and credit-constrained agricultural economy. The Agricultural Risk Intelligence tool, designed for commercial seed and crop science companies, provides granular evidence of crop stress throughout the growing season, weighted to each crop's biological calendar. This means, for instance, that heat stress during a critical pollination period for maize would be assessed differently than during early growth, offering precise insights into potential yield impacts. Crucially for Zambia, this product is already live for corn in key markets including Kenya, Tanzania, and Zambia, offering local seed companies the ability to strategically place appropriate crop varieties based on real-time seasonal forecasts rather than historical data. Complementing this is the Agricultural Credit Risk Intelligence, which equips agricultural lenders with field-level evidence on borrowers across the entire lending lifecycle. This innovative solution moves beyond traditional credit files and self-reported data, providing farm-level yield history and clear explanations for forecasts. For Zambian financial institutions, which often face challenges in assessing the inherent risks of agricultural lending due to lack of collateral, limited farmer data, and the risky nature of production, this platform could be transformative. It allows for proactive assessment and monitoring of crop risk at the farm scale, a significant im