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Updated: Jun 4, 2026
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
Rethinking yield stability through phenotypic plasticity and its link to modern statistical methods
Victor Sadras1,2,3, Matthew Welsh4, Beata Sznajder5,6
1South Australian Research and Development Institute, Kaurna Country, Australia.
Journal of Experimental Botany
|June 3, 2026
Summary
This study links phenotypic plasticity and factor analytic models to understand crop yield stability. Adaptive plasticity enhances yield, while maladaptive plasticity reduces it, offering biological meaning to statistical models.
Area of Science:
- Crop science
- Evolutionary biology
- Quantitative genetics
Background:
- Advocacy for yield stability often lacks clear definitions and consistent methodologies.
- Phenotypic plasticity, a concept from developmental biology, ecology, and evolution, is increasingly relevant in crop science.
- Factor analytics offers advanced statistical modeling for genotype-by-environment interactions in multi-environment trials.
Purpose of the Study:
- To connect the concepts of phenotypic plasticity and factor analytics in the context of crop yield.
- To define and assess agronomically adaptive plasticity for superior crop performance across environments.
- To provide biological interpretation for complex analytical models used in crop science.
Main Methods:
- Reviewing existing research on phenotypic plasticity and defining agronomically adaptive plasticity.
- Developing and outlining factor analytic models for analyzing multi-environment trial data.
- Mathematically and empirically linking phenotypic plasticity with factor analytic model outputs.
Main Results:
- Phenotypic plasticity in cereal yield shows a positive correlation with overall performance when adaptive, according to factor analytic models.
- A negative correlation is observed between phenotypic plasticity and overall performance when plasticity is maladaptive.
- Percentile-plasticity plots were utilized to evaluate the agronomic significance of plasticity.
Conclusions:
- Phenotypic plasticity adds essential biological context to statistical factor analytic approaches.
- Biologically informed statistical methods are crucial for critically evaluating agronomic claims, such as the benefits of yield stability.
- This research challenges simplistic narratives by integrating developmental biology concepts with advanced statistical analysis.
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