By understanding seeding date and location, Parametrics.ag monitored the development of the canola and automatically began assessing the impact of daytime temperatures exceeding 29 °C and nighttime temperatures exceeding 18 °C and assessing claims in real-time during its flowering period. Our canola heat blast coverage paid producers up to $100 dollars per acre all without needing to measure the claim against what farmers had “in the bin”. 

Layering parametric coverage into seed contracts added market value for the producer’s premium canola seed by creating coverage specifically for its growers. The same principle can be applied to other major crops where an environmental factor can be monitored and tied to the performance of a crop and Parametrics.ag is actively working to assess the viability of other parametric indices for other crops to provide simple and effective coverage without the need for complicated overhead and claims processes.  

With Parametrics.ag performing automated temperature monitoring on a per-township-basis in the days following fungicide application, the chemical manufacturer was able to focus on other aspects of the program, reducing the internal efforts required to administer the fungicide heat blast warranty program. 

By working with Parametrics.ag, chemical producers with environmentally sensitive products can create warranty programs that ensure that farmers are protected when environmental factors result in the need to re-apply their product. The solution can be tailored to the risks that affect the chemical and ensure that your customers are getting the most out of their application. 

While environmental monitoring using traditional weather stations only allows for roughly 200 points of measurement across western Canada, our solution creates 40,317 points of accurate rainfall measurement. Our innovative solution reduces the reliance on low-resolution monitoring and dramatically improves the quality of the available data. 

All grassland health monitoring is automated and conducted in real-time. This allows the model to dynamically update, empowering us to draw insights about grassland health and the risks associated with adverse weather patterns as the season progresses, rather than through a post-mortem after the season is over.  

Our team leveraged machine-learning algorithms to analyse a combination of historic production performance, soil, weather, and geospatial data leveraging to drive meaningful insights about the product’s risk profile. Our findings indicated significant variations in risk across its geographical distribution. 

Through our analysis, Parametrics.ag developed a Land-Based-Underwriting methodology to ensure that risk is being accounted for where is it most prevalent and empower Global Ag Risk Solutions to make sound underwriting decisions and offers of insurance.