Integrating Farmer Motivations into Phenological Models: Impacts of Sowing Decisions on German Maize

The impact of climate change on agricultural yields and risk management is fundamentally tied to shifts in crop phenology. As warming climates accelerate plant development, adjusting sowing dates has emerged as a critical adaptation strategy for maintaining productivity. However, the drivers of future sowing decisions remain a subject of debate. Current modeling strategies, ranging from prescribed planting dates and temperature thresholds to sophisticated decision trees, often imply different assumptions regarding farmers' goals. Some approaches assume farmers seek to maximize the growing period to optimize yields, while others suggest they prioritize reducing inter-annual variability to ensure business stability.

This study investigates the determinants of maize sowing dates in Germany. While historical observations and rule-based models highlight temperature as the primary driver in Central Europe, recent findings suggest that integrating soil moisture may offer higher predictive power. To bridge the gap between theoretical modeling and practical management, we surveyed German farmers to identify the primary motivations behind their planting decisions.

Based on these insights, we developed a planting model that incorporates rolling temperature averages, defined "earliest possible" sowing dates, and soil moisture constraints. This decision-making framework was linked to a Bayesian phenological model to simulate the future development of maize in Germany, assuming the use of modern-day cultivars in a shifting climate.

Our results shed light on the factors that determine farmers' motivations for choosing specific planting dates, as well as the effects these decisions have on crop phenology. By refining how we model the "human factor," we provide a more robust assessment of climate change impacts on German maize production.