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Updated: Oct 5, 2025
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
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Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency
Larry M York1, Marcus Griffiths2, Tai McClellan Maaz3
1Center for Bioenergy Innovation and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Current Opinion in Biotechnology
|February 1, 2022
Summary
Improving nutrient use efficiency (NUE) in crops requires integrating plant traits like root acquisition and metabolic efficiency. This approach enhances yield beyond simple nutrient ratio measurements.
Area of Science:
- Agricultural Science
- Plant Physiology
- Nutrient Management
Background:
- Nutrient use efficiency (NUE) is conventionally measured by yield relative to soil nutrient availability, overlooking crucial plant traits.
- Key plant traits influencing NUE include root acquisition efficiency, shoot radiation use efficiency, and metabolic efficiency.
Purpose of the Study:
- To highlight the importance of integrating plant traits for synergistic improvements in nutrient use efficiency (NUE).
- To discuss recent advancements in trait-focused research and their application in agricultural systems.
Main Methods:
- Reviewing progress in phenotyping root nutrient uptake and respiration.
- Examining genetic engineering strategies like reduced photorespiration.
- Identifying nutrient assimilation pathways and conceptualizing traits within agricultural systems.
Main Results:
- Integrating root, shoot, and metabolic efficiencies offers synergistic NUE improvements.
- Advancements in phenotyping, genetic engineering, and pathway identification are key.
- Contextualizing traits in agricultural systems, including crop mixtures, is vital for synchronizing plant demand with nutrient supply.
Conclusions:
- Synergistic improvements in nutrient use efficiency (NUE) are achievable by integrating plant traits.
- Future gains in NUE can be accelerated using simulation modeling and multi-objective optimization, moving beyond single-ratio selection.
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