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

Area of Science:

• Agricultural Science
• Plant Breeding
• Spectroscopy

Background:

• Grassland-based livestock production is sustainable but often lacks energy from roughage.
• Forage legumes like red clover could enhance energy content, reducing reliance on supplements.
• Efficient phenotyping is crucial for breeding high-starch red clover genotypes.

Purpose of the Study:

• To evaluate a non-destructive hyperspectral imaging approach for estimating leaf starch content in red clover.
• To enable efficient development of high-starch red clover genotypes.
• To assess the prediction performance of partial least square regression (PLSR) models.

Main Methods:

• Partial least square regression (PLSR) models were developed using cross-validation.
• Model performance was validated using an independent test set under controlled conditions.
• Leaf starch content was measured in milligrams per gram dry weight (mg g⁻¹ DW).

Main Results:

• The best cross-validated PLSR model explained 56% of the variation in starch content (RMSE = 17 mg g⁻¹ DW).
• Model performance decreased on the independent test set (RMSE = 29 mg g⁻¹ DW, R² = 0.36).
• Variable selection methods did not improve model performance.

Conclusions:

• Hyperspectral imaging shows potential for non-destructive estimation of red clover leaf starch content.
• This method could facilitate selection of high-starch red clover breeding material without plant destruction.
• Field validation is necessary to confirm the method's applicability in real-world breeding programs.