Summary
This study presents a remote sensing approach combining Sentinel-2 optical imagery with random forest regression to predict forage quality across UK grasslands, addressing the limitations of destructive field sampling. Calibrated on over 9,500 georeferenced observations collected between 2020 and 2022 at the North Wyke Farm Platform, the models achieved strong predictive accuracy (R² 0.77–0.86) for crude protein, water-soluble carbohydrates, and fibre fractions. The findings demonstrate that spatial variation between paddocks was more pronounced than seasonal variation, and that improved pastures exhibited superior forage quality compared with permanent pastures.
UK applicability
The calibrated models are directly applicable to UK temperate grassland systems with similar species composition and quality ranges to those at the North Wyke Farm Platform study site. However, the authors note that models should not be directly applied to forage systems with substantially different botanical composition or quality characteristics without recalibration.
Key measures
Crude protein (CP), water-soluble carbohydrates (WSC), neutral detergent fibre (NDF), acid detergent fibre (ADF); R² and RMSE model performance metrics; Sentinel-2 spectral reflectance data (visible, near-infrared, and red-edge bands)
Outcomes reported
The study developed and validated random forest regression models using Sentinel-2 satellite data to predict four forage quality attributes (crude protein, water-soluble carbohydrates, neutral detergent fibre, and acid detergent fibre) in UK grasslands. Model performance showed R² values between 0.77 and 0.86 with low RMSE values, demonstrating high predictive accuracy for scalable forage monitoring.
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