Michael Roth
Senior Originator
Weather & Agro
Munich Re

Oleksandr Artiushyn
Senior Originator
Weather & Agro
Munich Re

Climate change is ‘a harsh reality’ already threatening grain yields—yet ‘volume risk remains largely unhedged’ beyond the farm gate. While traditional tools fall short, ‘crop growth models offer an unexpected solution,’ enabling yield forecasts, tailored hedging strategies, and more resilient supply chains.

Climate change is no longer a distant threat, but a harsh reality that is already severely impacting agricultural production globally. In Eastern Europe, rising temperatures, erratic rainfall patterns, and other extreme weather events are increasingly jeopardizing grain yields, thereby disrupting entire supply chains. These climate-driven shifts not only compromise crop quality but also pose a more fundamental challenge: the risk of reduced hectare-yields and overall production volumes. Although various insurance programs are available to farmers, the volume risk faced by traders, off-takers, and processors remains largely unhedged, leaving the entire supply chain exposed and vulnerable to sudden disruptions.

Multiple factors explain why yield risk remains unhedged 

Today, there is a glaring gap in effective volume risk management beyond the farm gate. Unlike price risk, which is already mitigated within a standard process by almost any player by using forward and futures contracts, volume risk, caused by fluctuations in crop yield, is far more complex to address. Several factors contribute to this shortfall:

Firstly, many industry players are still unaware of the risk management instruments available. While price risk typically catches the attention of any CFO, yield risk often remains within the respective business unit and is considered something inherent which must be either managed through diversification or simply be accepted. Additionally, insurance is widely considered a cost centre rather than a tool for more efficient management of cash flows, assets, and equity.

Secondly, the standard approach to managing yield risk beyond the farm gate is through weather derivatives. However, they have not gained widespread adoption in the region. These contracts, which compensate buyers based on predefined weather conditions, are often perceived as too complex and abstract, making them difficult for stakeholders to understand and trust. Additionally, basis risk can be significant, as specific weather conditions are only one factor driving yields; factors such as soil quality and farming practices also have an equally important influence on yield and are ignored by a weather derivative. Lastly, the network of weather stations typically is not dense enough to capture the granularity of crop production.

Thirdly, more known alternatives such as insurance or futures contracts have either limited availability or fail by design. For example, traditional insurance products struggle to demonstrate their value effectively because they rely on historical payout comparisons. Given the increasing volatility due to changing weather patterns, historical data may no longer be a reliable indicator of future risk. This uncertainty makes it difficult to justify the cost of such a solution. It is possible to use futures contracts not only for hedging price risk but also to lock in volumes. However, these do not directly address volume risk and the use of futures for that purpose can expose buyers and sellers to unintended price fluctuations and associated margin calls. If production levels fall unexpectedly, hedgers may find themselves locked into contracts that do not align with their actual output.

Going forward: Rising awareness and repackaging established technologies

Proactive management of yield risk requires both awareness of the issue and knowledge of potential solutions. On the one hand, the increasing frequency of extreme weather events is widely recognized, with such events now appearing to occur almost annually. On the other hand, despite awareness of technological advancements, the practical applications of these technologies in transforming data into tangible solutions often remain unclear.

One notable example of technological innovation is the successful use of satellite imagery in predicting crop yields, which has achieved impressive results. When combined with AI, the results for yield protection can be even more impressive, provided the data is well-calibrated to ground conditions. However, a significant drawback of these recent advances in remote sensing is that they do not allow for sufficient evaluation of the past, as the necessary high-resolution satellite imagery has only been available for a couple of years. As a result, those who might want to structure a hedge using this data would be hindered by the inability to back-test its value over a prolonged period, limiting the reliability of such a strategy.

Crop growth models – an unexpected solution

However, traditional crop growth models can offer an unexpected solution. These are usually open-source widely accepted scientific models that simulates plant growth under various climatic conditions, taking into account local soil parameters and management practices, from individual planting dates and fertilizer amounts to specific varieties used. In other words, one can think of it as a digital clone of the underlying farmland. 

One of the most well-known crop growth models is DSSAT (Decision Support System for Agrotechnology Transfer) which has been used for over 40 years. Unlike remote sensing models that look at the data from above, DSSAT takes a ground-up approach to model all natural processes affecting plant growth, such as soil water availability, nitrogen intake, plant available water, etc. This method can provide yield scenarios that reach back as far as 1980. Utilizing the results of these models does not require specialized expertise in operating the models themselves, making the insights and predictions more accessible to a broader range of users. The practical application is simple: farming practices and risk exposure are recorded, and a third-party provider operates the crop growth model using the client’s data along with independent weather data to estimate yields. 

A fleet of applications 

Firstly, by simulating various environmental conditions and input scenarios, these simulations assist in evaluating the suitability of a new plot of land or an entire portfolio of different farm locations for cultivation, as well as how yield may fluctuate over time. This enables more informed decisions when expanding operations or exploring new supply sources. With precise yield forecasts, both producers and aggregators can utilize the simulation’s predictions to develop cropping strategies that maximize production based on the anticipated outcomes for each season. 

Secondly, the yield forecasts can be incorporated into trade plans, assisting farmers, off-takers, and processors in determining optimal times and methods for selling their crops to achieve maximum profit.

Thirdly, as growing seasons unfold, models provide continuous yield forecasting, allowing the user to modify their plans to mitigate risks and capitalize on opportunities. This real-time monitoring ensures that decision-making remains flexible and responsive to changing conditions. 

Lastly, by using modelled yield data and resulting indices, farmers structure financial products that match their specific risk profiles. For example, the yield forecast can be used as an underlying index for a derivative transaction; if the crop yield falls below a certain level (strike), the buyer is compensated for each unit with a predefined amount.

While it may be overstating to say the aforementioned solution has become a standard, professional underwriters of weather risk like Munich Re have enabled significant transactions in North and South America with producers, cooperatives and grain offtakes in recent years and are observing an increasing demand for yield hedges in Eastern Europe as well.

The path forward

Europe’s grain industries can adapt to the new climate reality and address the historically overlooked yield risk by embracing innovative risk management tools. While volume risk hedging has been historically overlooked, solutions like DSSAT-based modeling can provide a viable, science-backed approach to ensuring supply stability.