Speaker A
Munich RE offers crop yield hedges based on model yield indices. The most commonly used model here is the DESAT model. In this video, we explain how DESAT works, what it is used for, and how well it performs. It is an independent crop modeling tool that simulates all important physical and biological processes impacting plant growth and crop yield. This sounds complicated, but its basic functionality can be explained easily. DESAT always begins with a collection of input data tailored to specific locations, including soil type and quality, ensuring the simulation reflects real-world growing conditions. In this spirit, it also requires details on the crop types and genetics. Lastly, farming practices play a major role in shaping yields. In a nutshell, the model considers the very same issues that producers would address when planning the upcoming season: planting dates, fertilizer applications, tillage methods. With these few inputs, DESAT is already ready to simulate the underlying physiological and biological processes impacting plant growth. This starts with the interaction of the three major players: soil, plant, and the atmosphere. For example, it simulates daily soil temperature and evapotranspiration. Then, based on this, it simulates plant growth along with the crop's nutrient demand and how the soil dynamics allow the plant to take in these nutrients. In simple words, DESAT simulates the interaction of available water, the major nutrients like nitrogen and phosphorus, and the soil organic carbon. Of course, all these processes are influenced by one big factor: the weather's variability throughout the season. For this, the major drivers of plant growth—temperature, precipitation, and solar radiation—are taken into account. This data also comes from independent data providers. Finally, DESAT brings all of this information together. Think of it as a digital replica of the underlying cropland simulating crop yields with real weather data. These insights can be used in various ways. Firstly, the calculation of in-season actual yields during and at harvest. These can be used for monitoring and planning purposes such as grain marketing or logistics. And, of course, it can also be used for a crop yield hedge. Secondly, it can also recalculate actual historical yields or "as if" scenarios that show how yields might have differed under different farming practices or weather conditions, allowing the user to compare DESAT yields. Now that you know how it works, let's ask the obvious question: What is the quality of the model? When we look at the performance on a Brazilian corn farm, the results can be remarkably strong. In average years, we see some deviations, but in challenging years, typically those marked by drought and high temperatures, the model performs with very high accuracy. These deviations mainly stem from the quality of the input data. Strong results depend on having accurate, high-resolution weather and soil data combined with reliable details on management practices. When these inputs are robust, as you can see, the model delivers in the context of crop yield hedges. Consider the three key benefits of DESAT. It is a sound, proven model with robust results capturing all major weather risks. Although the simulations are complex, the required inputs are simple and reflect real-world farming practice. It supports both in-season calculations and back testing, letting buyers evaluate the hedge's value using their own historical data.
