Spectral reflectance estimated genetic variation in hybrid turf bermudagrass

Abstract

High throughput phenotyping (HTP) utilizing both remote and proximal sensing technologies has emerged as a vital tool for evaluating the biophysical characteristics of turfgrass. This study was conducted to assess the genetic diversity of hybrid turf bermudagrass using spectral reflectance indices and use of HTP for germplasm enhancement. A total of 50 accessions of the hybrid bermudagrass (Cynodon dactylon × C. transvaalensis) were grown in the greenhouse in three replications. The spectral data were gathered using a height independent active crop canopy sensor, 'RapidScan CS-45', which measures canopy reflectance at the wavelengths of 670 nm, 730 nm, and 780 nm. The reflectance data were used to derive three indices related to canopy photosynthetic area and other three related to chlorophyll content. All vegetation indices showed significant genotype-to-genotype variation. Ten superior genotypes were identified using the multi-trait genotype-ideotype distance index (MGIDI) as a selection differential. On 48 of the genotypes that were established in the field in two replications, establishment rate and winter color data were also gathered. The results of a linear regression analysis demonstrated the importance of spectral vegetation indices (SVI) for the turfgrass quick establishment (percentage area coverage) and winter color retention. This study brings attention to the potential use of the proximal sensing in turfgrass germplasm enhancement for establishment speed, aesthetic value, and mild-winter color retention.

Plant phenotyping relevance

芝草遺伝資源評価において、近接型キャノピーセンサーによるスペクトル取得と植生指数算出が主要な表現型評価手法として用いられているため。

Code and data availability

The paper's spectral reflectance phenotype datasets (RapidScan CS-45 reflectance, vegetation indices, establishment and winter color records) are not publicly deposited; the authors state they are available only from the corresponding author on reasonable request. No public code, images, or model assets are mentioned.

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