• Introduces a decision-table, state-triggered management framework to reduce structural bias in impact assessment modeling. • Provides a reproducible workflow to derive phenology-based triggers from historically consistent management timing. • Demonstrates that fixed calendar assumptions can change both the magnitude and direction of projected impacts. • Quantifies implications for key IA-relevant endpoints, including crop yields and nitrogen losses under climate scenarios.
Eini et al. (2026) studied this question.