2026-10-14T20:04:37Z https://www.scidb.cn/oai

10.57760/sciencedb.34214 2026-04-21T11:03:20Z

2026-04-21 Global gridded soybean yield dataset for future warming scenarios using emergent constraints doi:10.57760/sciencedb.34214 en Reliable projections of future soybean yields under global warming are essential for food security and climate adaptation planning. 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. The constrained soybean yields also show improved agreement with both subnational statistics and independent gridded yield datasets. This dataset can serve as a more reliable input for integrated assessments of climate change impacts on global soybean market. Soybean yield; Climate change; Multi-model ensemble; Uncertainty reduction Jiani Fang Cancan Qiao Changxiu Cheng PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank