Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status.

Abstract

Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.

Plant phenotyping relevance

葉の光学特性から複数の生理・構造形質を非破壊かつ高スループットに推定する分光法と予測モデルを開発・独立検証しており、フェノタイピング手法が中心である。

Code and data availability

The paper states that its leaf spectra, biochemical trait data, and PLSR R code were made publicly available via the EcoSIS spectral library (doi:10.21232/C2GM2Z). This is a paper-specific, public phenotyping asset (spectra–trait dataset plus analysis code), but no URL is present in the allowed_urls list, so it cannot