Global gridded soybean yield dataset for future warming scenarios using emergent constraints

However, current yield projections from multiple climate-crop models are subject to substantial uncertainty (i.e., inter-model spread), thereby limiting the reliability of subsequent adaptation decisions. Here, we developed a global gridded soybean yield dataset with reduced uncertainty under 1.5℃ and 2.0℃ warming scenarios.

The raw multi-model yield simulations from different emissions scenarios were first aligned to consistent global warming levels. The emergent constraint approach was then applied to further constrain the multi-model projections using observed historical yields, leveraging robust emergent relationships between simulated historical and future yields across models. The resulting constrained projections exhibit a marked reduction in uncertainty, with a median reduction exceeding 65% relative to the raw multi-model simulations.