Mass Flow Sensing and Yield Mapping for Forage Mowing Equipment

Yield monitoring in forage production is typically limited to chopping or baling operations, where spatial resolution is often reduced by windrow merging. This study evaluated the feasibility of estimating mass flow rate (MFR) and generating spatial yield maps at the mowing stage using sensors integrated into a windrower. Conditioning roll speed, swath shield impact force, and the displacement of spring-loaded vanes (fingers) in the crop flow were evaluated during alfalfa harvest and calibrated against measured MFR. Model performance was assessed using cross-validation, and spatial fidelity was evaluated using experimental variograms and kriged yield maps. The average MFR was 19 kg·s−1 with a range of 4 to 55 kg·s−1. Conditioning roll speed provided the most robust and transferable predictor of MFR (R2 = 0.89, RMSE = 3.4 kg·s−1), consistently outperforming impact force (R2 = 0.70, RMSE = 1.9 kg·s−1) and finger displacement (R2 = 0.82, RMSE = 4.3 kg·s−1), which were more sensitive to machine dynamics and sensor placement. Validation of the roll-speed model using an independent dataset resulted in an R2 = 0.87 and RMSE of 2.62 kg·s−1. Yield maps derived from roll-speed-based models exhibited clear spatial structure with correlation lengths of approximately 25–40 m, whereas the finger displacement model exhibited higher nugget effects. Yield mapping with the forage harvester showed reduced spatial fidelity compared to mowing stage estimates, as windrow merging prior to chopping caused spatial averaging that diminished recoverable fine-scale yield variability. These results demonstrate that yield monitoring at the mowing stage enabled yield estimates to complement downstream harvest data and improve characterization of within-field yield variability.