Pedigree‐management‐flight interaction for temporal phenotype analysis and temporal phenomic prediction

Abstract

AbstractUnoccupied aerial systems (UAS, aka drones) provide high dimensional temporal phenotype data for predictive plant breeding and genetic dissection. Methods to assess temporal phenotype data are an emerging need to predict temporal breeding values of genotypes. Here a novel interaction design was developed and evaluated to include drone flight dates as a component into the mixed model; allowing the temporal changes of drone image derived traits of maize hybrids across different flight dates as well as different management conditions to be monitored. Across 2017 and 2019 respectively, 228 and 100 maize hybrids were grown under two types of management (optimal and late plantings). Seven drone surveys were conducted over each management in 2017 while five drone surveys were conducted over each management in 2019. Temporal plant height (canopy height measurements, CHM) and normalized green‐red difference index (NGRDI) were extracted from each drone survey and used as phenotype data to evaluate the interaction design. Day of flight effects explained the highest amount of total variation for grain yield in the interaction model, meaning the majority of phenotypic variation of CHM and NGRDI occurred across growth with a unique temporal trajectory in each management system. Temporal repeatability values remained higher than 0.5 for CHM and NGRDI in each year. Temporal CHM and NGRDI breeding values of maize hybrids were combined in ridge and lasso regression prediction models. Yield prediction ability of untested genotypes in untested environments were predicted higher by using pedigree × management × flight (PMF) and pedigree× management (PM) interaction results (∼0.34 and 0.52 in 2017 and 2019). Combining environment specific phenomic data (PMF plus PM) gave a larger improvement in yield prediction when the tested and untested environments were less similar. Overall, combined temporal phenomic data could moderately predict grain yield under the most challenging predictive breeding scenario, untested and unrelated genotypes in untested environments.