Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments

A primary selection target for wheat (Triticum aestivum) improvement is
grain yield. However, the selection for yield is limited by the extent of
field trials, fluctuating environments, and the time needed to obtain
multiyear assessments. Secondary traits such as spectral reflectance and
canopy temperature (CT), which can be rapidly measured many times
throughout the growing season, are frequently correlated with grain yield
and could be used for indirect selection in large populations particularly
in earlier generations in the breeding cycle prior to replicated yield
testing. While proximal sensing data collection is increasingly
implemented with high-throughput platforms that provide powerful and
affordable information, efficient and effective use of these data is
challenging. The objective of this study was to monitor wheat growth and
predict grain yield in wheat breeding trials using high-density proximal
sensing measurements under extreme terminal heat stress that is common in
Bangladesh. Over five growing seasons, we analyzed normalized difference
vegetation index (NDVI) and CT measurements collected in elite breeding
lines from the International Maize and Wheat Improvement Center at the
Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored
several variable reduction and regularization techniques followed by using
the combined secondary traits to predict grain yield. Across years, grain
yield heritability ranged from 0.30 to 0.72, with variable secondary trait
heritability (0.0–0.6), while the correlation between grain yield and
secondary traits ranged from−0.5 to 0.5. The prediction accuracy was
calculated by a cross-fold validation approach as the correlation between
observed and predicted grain yield using univariate and multivariate
models. We found that the multivariate models resulted in higher
prediction accuracies for grain yield than the univariate models. Stepwise
regression performed equal to, or better than, other models in predicting
grain yield. When incorporating all secondary traits into the models, we
obtained high prediction accuracies (0.58–0.68) across the five growing
seasons. Our results show that the optimized phenotypic prediction models
can leverage secondary traits to deliver accurate predictions of wheat
grain yield, allowing breeding programs to make more robust and rapid
selections.