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

Abstract

This study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system in response to factorial low and high nitrogen (N) and phosphorus (P) application. HTP has not previously been used to examine pasture species in mixture. We used red-green-blue (RGB) imaging to obtain smoothed projected shoot area (sPSA) to predict absolute growth (AG) up to 70 days after planting (sPSA, DAP 70), to identify variation in relative growth rates (RGR, DAP 35-70) and detect overyielding (an increase in yield in mixture compared with monoculture, indicating facilitation) in a grass-legume model pasture. Finally, using principal components analysis we interpreted between species changes to HTP-derived temporal growth dynamics and nutrient uptake in mixtures and monocultures. Overyielding was detected in all treatments and was driven by both grass and legume. Our data supported expectations of more rapid grass growth and augmented nutrient uptake in the presence of a legume. Legumes grew more slowly in mixture and where growth became more reliant on soil P. Relative growth rate in grass was strongly associated with shoot N concentration, whereas legume RGR was not strongly associated with shoot nutrients. High throughput, image-based phenotyping was a useful tool to quantify growth trait variation between contrasting species and to this end is highly useful in understanding nutrient-yield relationships in mixed pasture cultivations.

Plant phenotyping relevance

RGB画像による高スループット表現型計測を用いて、投影シュート面積から成長形質を抽出・定量する手法の実質的な適用研究であり、表現型取得が中心的です。

Code and data availability

The authors deposited the manuscript's underlying phenotype data (sPSA/growth trait measurements from the HTP experiment) in Figshare, with a provisional DOI (10.25909/12895121) and an access link. This is a paper-specific public data asset. The R packages cited (dae, growthPheno, asremlPlus) are generic third-party CR