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

Abstract Screening new cultivars through yield trials in multiple environments has improved crop yields, but the accumulated data from these trials has not been effectively reused. We propose a simple method that quantifies cultivar-specific characteristics of productivity using two regression coefficients for yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar were expressed using a unique linear regression in response to the theoretical potential yield calculated by a weather-driven crop growth model called the “YpCGM method.” We applied this analysis to 72,510 independent datasets from yield trials of rice (Oryza sativa L.) that used 237 core cultivars measured in breeding programs at 110 locations in Japan over 38 years. The coefficients of β and α differed among the 237 cultivars, with values ranging from 2.5 to 7.3 t/ha and from –0.23 to +0.95, respectively. Genomic prediction validated the values of these two coefficients by a 10-fold cross-validation based on pedigree information and the rice genome’s 91,800 single-nucleotide polymorphisms. The YpCGM method can upcycle accumulated yield data for use in genetic gain analysis and genome-wide association studies to guide future breeding programs in developing new cultivars suitable for the world’s changing climate.