Upcycling rice yield trial data using a weather-driven crop growth model.

ABSTRACT: Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Y_p) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110

SUBMITTER: Shimono H 

PROVIDER: S-EPMC10362053 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature