Positive effects of public breeding on US rice yields under future climate scenarios

• 成果类型: Article
• 署名作者: Wang, Diane R.; Jamshidi, Sajad; Han, Rongkui; Edwards, Jeremy D.; Mcclung, Anna M.; Mccouch, Susan R.
• 署名单位: Purdue University System; Purdue University; University of California System; University of California Davis; United States Department of Agriculture (USDA); Cornell University; Bayer AG; Bayer CropScience
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11182
• DOI: 10.1073/pnas.2309969121
• 发表日期: 2024-03-26
• 关键词: cultivar shifts; temperature; hybrid; GROWTH; TRENDS; maize; CHINA; MODEL; grain

摘要

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. ricegrowing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county -level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county -level allele frequencies. These allele frequencies are used together with county -level weather and yield data to develop ten machine learning models for yield prediction. A two -layer meta -learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice -growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.