From Fixed Calendars to Dynamic Triggers: Climate-Responsive Field Management with SWAT+ Decision Tables

Climate-change impact assessments using agro-hydrological models often assume that farmers stick to their historical planting and harvest calendars under future climate conditions. This fixed-calendar assumption conflicts with observed and expected shifts in crop phenology and management, leading to errors in simulated crop timing, yields, and water use. Here, we develop and test a generic workflow to replace static calendars with dynamic, state-triggered crop management in SWAT+ using decision tables. Focusing on winter wheat and corn silage in a rainfed catchment in eastern Germany, we first run a conventional, date-based SWAT+ setup and analyze the management log to determine the heat units and weather conditions under which farmers actually plant, harvest, till, and fertilize. Empirical distributions of base-0 potential heat units, days since planting/harvest, and recent precipitation are converted into compact decision-table guards. These guards, mainly expressed as PHU windows and dry-day constraints, are tuned so that dynamic management reproduces historical planting and harvest dates within about one week, while maintaining baseline yield performance. Next, we run the decision-table model with three contrasting late-century EURO-CORDEX climates (cool–dry, cool–wet, warm–wet), without changing crop parameters or management intensities. Under cool scenarios, winter wheat maintains a long growing season of approximately 313–314 days, but under a warm–wet climate, later autumn planting is followed by an earlier midsummer harvest, shortening the season to roughly 287–290 days. Corn silage shows marked advances in planting (up to about three weeks earlier in the warm–wet scenario) while harvest remains anchored near early September, lengthening the growing period from ≈150 to ≈170 days across scenarios. Yields for corn silage stay high and fairly stable, whereas winter wheat yields show modest scenario-dependent changes in the average and more variation between years. The study demonstrates that SWAT+ decision tables can encode historically accurate, climate-responsive management directly from observed practices and apply it consistently under future climates. The proposed workflow provides a transferable model for representing adaptive cropping calendars, reducing structural bias, and enhancing the credibility of climate-change impact studies on crops and agricultural water management.