Yusuke Toda, Goshi Sasaki, Yoshihiro Ohmori, Yuji Yamasaki, Hirokazu Takahashi, Hideki Takanashi, Mai Tsuda, Hiromi Kajiya-Kanegae, Hisashi Tsujimoto, Akito Kaga, Masami Hirai, Mikio Nakazono, Toru Fujiwara & Hiroyoshi Iwata

Theoretical and Applied Genetics; Published March 9 2024

Key message

We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles.

Abstract

Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.

See https://link.springer.com/article/10.1007/s00122-024-04565-5

Fig.1: Explanation of the field experiments. a An ortho-mosaic image of the field obtained on August 25, 2018. The ortho-mosaic images were created for each treatment (WW/W0). Blue circles indicate measurement points of soil moisture. Colors represented groups of points that were measured alternately. Green squares indicate plots in which the height of plants was measured manually. Circles and squares are drawn in fields of WW and W0, respectively, but soil moisture and plant height were measured in the same pattern of plots. b, c Ground-level images of treatments WW and W0. d Planting pattern of plots made of two rows of four plants (green dots) and separated by 80 cm. e Schematic illustration of watering patterns of four treatments.