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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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

Kengo Sakurai¹, Yusuke Toda¹, Kosuke Hamazaki¹

¹Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan.

Frontiers in Plant Science

|July 21, 2023

Summary

This study developed a new method using time-series multispectral data and random regression models to measure and model plant responses to drought. This approach effectively captures genetic variations in soybean drought tolerance, improving prediction accuracy.

Keywords:

Glycine max (L.) Merr.drought stress irrigation change multispectral (MS)plant response random regression model (RRM)single environmental trial time-series

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Area of Science:

Agricultural Science
Plant Physiology
Genetics and Genomics

Background:

Drought tolerance is a critical trait for crop yield under abiotic stress.
Existing methods for measuring and modeling plant responses to drought over time are not fully established.
Developing accurate methods is crucial for breeding resilient crop varieties.

Purpose of the Study:

To develop a novel method for measuring and modeling plant responses to irrigation changes using time-series multispectral (MS) data.
To evaluate the effectiveness of random regression models (RRMs) in capturing genetic variation for drought response in soybean.
To assess the accuracy of genomic prediction models for predicting drought tolerance traits.

Main Methods:

Evaluated 178 soybean accessions across three years (2019-2021) under various irrigation treatments (W5, W10, D10, D).
Collected time-series MS data using unmanned aerial vehicles during irrigation/non-irrigation cycles.
Developed random regression models (RRMs) and genomic prediction models using genetic RRM coefficients as secondary traits.

Main Results:

RRMs effectively captured plant responses to irrigation changes.
Genomic prediction models built on changing irrigation treatments showed higher accuracy (r=0.44-0.49) than those on continuous drought (r=0.34-0.44) in 2020-2021.
Predicting across years in changing irrigation treatments yielded a 42% higher accuracy (r=0.46) compared to simple genomic prediction (r=0.32).

Conclusions:

The developed RRM method using time-series MS data is effective for measuring and modeling plant responses to drought.
This approach successfully captures genetic variation related to drought tolerance in soybean.
The findings provide a foundation for improving breeding strategies for drought-resilient soybean varieties.