A Non-destructive Method to Quantify Leaf Starch Content in Red Clover.

Abstract

Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes. We assessed prediction performance of partial least square regression models (PLSR) using cross-validation, and validated model performance with an independent test set under controlled conditions. Starch content of the training set ranged from 0.1 to 120.3 mg g -1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g -1 DW. Model performance decreased when applying the trained model on the independent test set (RMSE = 29 mg g -1 DW, R 2 = 0.36). Different variable selection methods did not increase model performance. Once validated in the field, the non-destructive spectral method presented here has the potential to detect large differences in leaf starch content of red clover genotypes. Breeding material could be sampled and selected according to their starch content without destroying the plant.

Plant phenotyping relevance

レッドクローバー葉のデンプン含量という植物形質を、非破壊ハイパースペクトル画像とPLSRで推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper's data availability statement points to a Zenodo deposit containing the study's spectral and starch datasets. The simplerspec R package (Baumann 2019) is cited as a generic tool, not a paper-specific asset; the pls package is a generic CRAN library.