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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
adaptationbalancebiascultureheuristicsoatprospect theoryreaction normwheat

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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.