Simulating economic impacts of droughts on agriculture using DroughtMAS

In Central Europe and Germany, climate change increases the risk of temperature anomalies, heat waves, and droughts. The most profound and direct impacts of such risk will be on agriculture and food systems. Climate change will adversely impact crop yields and jeopardize harvests, resulting in substantial crop production and economic losses. The expected increase in the frequency and severity of droughts in the future due to climate change can lead to significantly higher annual economic losses. To mitigate these impacts, cultivation regimes, crop rotation, and even the entire production patterns would need to be adapted. Hence, it is imperative to understand the management of risk in crop production and its role in drought adaptation in light of predicted impacts and contribute to evidence for adaptation policies.

To achieve this objective, we develop a spatial multi-agent system (MAS) model, DroughtMAS, using an Econometric Mathematical Programming (EMP) approach. The model simulates land-use adaptation to drought conditions, estimate the damages of droughts, and assesses risk management tools and strategies. The MAS model captures the biophysical and agro-economic heterogeneity of German agriculture through individually parameterized 401 land-user agents at a sub-national scale. Cropping behavior is calibrated with land-use data from high-resolution remote sensing analyses and public records. The economic parameters ground the model in a policy-relevant context while the statistical functions capture the impacts of biophysical factors on crop production. These yield functions enable the model to respond to soil moisture changes from observed data or projections from hydrological models.

The current modeling efforts are focused on extending the model to better reflect a farmer’s cropping decision at the time of planting. Specifically, using empirical data, we estimate the farmer’s expected yield factor at the planning/planting stage expressed as a function of previous drought-year yields for major agricultural crops in Germany. The aim is to explore how the dynamic interplay between the loss of income in previous droughts and resultant farmer cropping decisions affects adaptation to droughts in agriculture.  

We present the first analysis of the extension to demonstrate the ability of the model to capture farmer cropping decisions during drought and quantify their economic impacts on agriculture in Germany. The results provide bottom-up estimates of the economic damages of droughts accounting for much-needed short-run behavioral dynamics of adaptation. This provides a versatile validated cropping simulation model that can be used for realistic projections of future drought impacts of farm-specific changes aggregated at a national scale. The model also presents a spatiotemporal pattern of these impacts, showing the potential for this model to inform targeted policy interventions. The DroughtMAS provides a platform to capture additional adaptation behaviors (e.g. drought-resilient crops, irrigation systems) and integrate with other models that require empirically validated inputs about various agricultural decision-making conditions.