Background: 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.
Results: We assessed prediction performance of partial least square regression models (PLSR) and validated model performance with an independent test set. 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 applied to the independent test set (RMSE = 29 mg g-1 DW, R2 = 0.36). Different filtering methods did not increase model performance.
Conclusion: The non-destructive spectral method presented here, provides a tool to detect large differences in leaf starch content of red clover. Breeding material can be sampled and selected according to their starch content without destroying the plant.