High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture

Area of Science:

• Agricultural Science
• Plant Biology
• Ecology

Background:

• Pasture productivity is influenced by species interactions and nutrient availability.
• Understanding nutrient dynamics in mixed pastures is crucial for sustainable agriculture.
• High throughput, image-based phenotyping (HTP) offers novel approaches to study plant growth.

Purpose of the Study:

• To apply HTP for analyzing growth patterns and nutrient uptake in a model mixed-pasture system.
• To detect facilitation and overyielding in grass-legume mixtures under varying nitrogen and phosphorus levels.
• To interpret species-specific growth dynamics and nutrient relationships using HTP data.

Main Methods:

• Utilized red-green-blue (RGB) imaging to measure smoothed projected shoot area (sPSA) for predicting absolute growth (AG).
• Calculated relative growth rates (RGR) and identified overyielding in a grass-legume model pasture.
• Employed principal components analysis to interpret HTP-derived temporal growth dynamics and nutrient uptake.

Main Results:

• Overyielding was consistently detected in all treatments, driven by both grass and legume components.
• Grass exhibited faster growth and enhanced nutrient uptake in the presence of legumes.
• Legume growth was slower in mixtures, particularly when reliant on soil phosphorus (P).

Conclusions:

• HTP is effective for quantifying growth trait variations in contrasting species within mixed pastures.
• The study provides insights into nutrient-yield relationships and facilitation mechanisms in mixed pasture cultivations.
• HTP facilitates a deeper understanding of plant interactions and resource utilization in complex agricultural systems.