The overarching goal of this project is to increase soybean protein production in non-optimal environmental conditions. Temperature-stressed soybean plants show low germination rates, growth delay, and reduced photosynthesis, yield and seed protein production. Temperature stress-tolerant crops are difficult to develop through conventional breeding. This multidisciplinary research uses state-of-the-art phosphoproteomics analysis, genotypic data and physiological information together with machine learning to link key post-translational regulators with the desired physiological and agronomic outcomes, like stable germination and increased yield and protein production during temperature stress. Research aims to generate temperature stress data for predictive model input, identify key phosphomarkers that predict temperature stress response and validate phosphomarkers for use in applied breeding.

Key Benefactors:
farmers, agronomists, extension agents, soybean breeders, seed companies