Climate, Rotation, and Tillage Impacts on Soybean Yield Gains in a 50‐Year Experiment

Understanding how interactive management practices and climatic behavior influence soybean [Glycine max (L.) Merr.] productivity is imperative to inform future production systems under changing climate. This study examined eight unique long-term production systems from 1975 to 2024 on a fertile rainfed Mollisol in Indiana, USA. Using this 400-unit field-scale dataset comparing soybean in monocropping versus following maize (Zea mays L.) across four tillage intensities (Moldboard Plow, Chisel, Ridge/Strip-Till and No-Till), we investigated how these practices interact with each other and with weather patterns. Our focus was on the spring (1 April-20 June) and summer (21 June-30 September) periods, and their effects on final yields and morphometric plant phenes. Soybean yield results during the 50-year period, when spring temperatures increased by 1.6°C, reflected (i) consistently positive yield responses to higher spring temperatures, at the rate of 796 kg ha^-1/°C, especially in monocropping, despite concurrent development of wetter springs and drier summers; (ii) improved yield resilience in the No-Till versus tilled systems; (iii) average soybean yields rotated with maize were 7.7% above those in monocropping but varied widely with tillage and year; and (iv) yield gain rates over time averaging 29.5 kg ha^-1 year^-1 for monocropping and 25.6 kg ha^-1 year^-1 for rotation. Plant height at 4- and 8-weeks post-planting was more influenced by air temperatures than precipitation, but final yield differences among tillage intensities were proportionately much smaller than relative soybean height differences in spring. These findings of consistent yield gains across multiple rotation and tillage regimes, despite changing climate factors, can inform actionable strategies for sustainable food production in future warming climate scenarios. Additionally, the unique rotation/tillage outcomes for 50 years provide a unique baseline for process-based crop model calibration to enhance our ability to design future cropping systems.