AUTHOR=Sakurai Kengo , Toda Yusuke , Hamazaki Kosuke , Ohmori Yoshihiro , Yamasaki Yuji , Takahashi Hirokazu , Takanashi Hideki , Tsuda Mai , Tsujimoto Hisashi , Kaga Akito , Nakazono Mikio , Fujiwara Toru , Iwata Hiroyoshi 

TITLE=Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

JOURNAL=Frontiers in Plant Science

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1201806

DOI=10.3389/fpls.2023.1201806

ISSN=1664-462X

ABSTRACT=<p>Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (<italic>Glycine max</italic> (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant’s stability under drought stresses. We built a genomic prediction model (<inline-formula><mml:math display="inline" id="im1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>MT</mml:mtext></mml:mrow><mml:mrow><mml:mtext>RRM</mml:mtext></mml:mrow></mml:msub><mml:mo> </mml:mo><mml:mtext>model</mml:mtext></mml:mrow></mml:math></inline-formula>) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of <inline-formula><mml:math display="inline" id="im2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>MT</mml:mtext></mml:mrow><mml:mrow><mml:mtext>RRM</mml:mtext></mml:mrow></mml:msub><mml:mo> </mml:mo><mml:mtext>models</mml:mtext></mml:mrow></mml:math></inline-formula> built in the changing irrigation treatments (<italic>r</italic> = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (<italic>r</italic> = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the <inline-formula><mml:math display="inline" id="im3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mrow><mml:mtext>MT</mml:mtext></mml:mrow><mml:mrow><mml:mtext>RRM</mml:mtext></mml:mrow></mml:msub><mml:mo> </mml:mo><mml:mtext>model</mml:mtext></mml:mrow></mml:math></inline-formula> across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (<italic>r</italic> = 0.46) was 42% higher than that of the simple genomic prediction model (<italic>r</italic> =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.</p>