Characterization and impact of agricultural machinery traffic on alfalfa forage harvested

Purpose: Harvest-related agricultural machinery traffic (AMT) can limit alfalfa production and persistence. This study aims to quantify the area affected by harvest AMT and assess its impact on alfalfa forage harvested (FH; i.e., total biomass harvested per area) under different soil preparation systems. Methods: Three experiments were conducted in Wisconsin, USA. In Experiment 1, GPS receivers were installed on all machines used in alfalfa silage harvest on a 650-ha commercial farm. Data were processed in QGIS to calculate the trafficked area, and a controlled traffic operation was simulated. In Experiments 2 (no-till) and 3 (moderate tillage), a 3-year field trial measuring FH was conducted using a randomized complete block design with three replicates. Traffic treatments included: No Traffic; 3- or 5-pass SILAGE (26 h post-harvest); and 3- or 5-pass HAY (72 h post-harvest). Results: GPS tracking showed that 46 ± 3.4% of the field area was traversed by AMT during a season, which decreased to 35 ± 0.6% under simulated controlled traffic. Under no-till, AMT had minimal impact on annual FH, although HAY traffic reduced FH by 7% compared to SILAGE in Year 3. Under moderate tillage, traffic decreased FH by 14% in the establishment year (Year 1), with HAY operations decreasing FH by 24% compared to SILAGE. Across Experiments 2 and 3, increasing the interval between harvest and AMT had a more negative effect on FH than the number of passes, whose effects were less consistent. Conclusion: Implementing controlled traffic and limiting traffic, particularly at longer intervals after harvest, may limit AMT impacts to alfalfa FH. Machinery traversed nearly half of the alfalfa field annually during silage production. Approximately 11% of the alfalfa fields may be covered by road tires annually. Controlled traffic minimizes spatial dispersion of machinery across the field. Agricultural machinery traffic can decrease alfalfa forage harvested by up to 24%. Longer intervals between alfalfa harvest and subsequent machinery traffic are more detrimental to forage harvested. This study combines GPS-based spatial analysis and field experiments to measure and understand the impact of harvest-related machinery traffic on alfalfa production. It aligns with Precision Agriculture by addressing within-field variability using geospatial technology and site-specific management methods, thereby improving system efficiency.