Data-driven resilience in agriculture

Finres produced the first ever high-resolution, geo-specific crop risk and adaptation prioritization analysis for the French agricultural sector.

In the world of climate science, one of the most complex challenges lies in producing accurate seasonal forecasts. These forecasts are critical for agriculture, as they enable farmers to make informed decisions about what to plant, when to plant, and how to manage their resources over the coming months. However, achieving accurate medium-range seasonal forecasts—those that predict conditions three to six months in advance—comes with significant computational challenges and costs for forecasters. At finres, we wanted to use our expertise in machine learning techniques to confront this challenge.

Climate change poses a growing challenge for the banking sector, with far-reaching impacts on economies and communities. With their central role in economic systems, banks will be at the forefront of how we respond. This is why there are increasing regulations and guidance on climate risk disclosure for banks. These are especially important to banks that finance and provide financial services to the agricultural sector.