Harvesting forage crops for silage utilizes multiple pieces of equipment to ensure rapid and economical production of feed. Machinery involved in the forage harvest process were instrumented to identify working states during 2 harvest seasons including 4 alfalfa (Medicago sativa L.) and one corn (Zea mays L.) silage harvest events per year. Self-propelled forage harvester utilization was found to be 61%. During working hours, idle time was found to be 18% to 23% for self-propelled forage harvesters and 10% to 20% for crop transportation machines. Crop transportation efficiency was defined and found to be dependent on transport capacity. Semi-tractor trailer transport vehicles were found to be the most efficient transporting 125 Mg km h⁻¹. A model of corn harvest for silage production, capable of predicting machine working status and total harvest time for a field, using a single harvester, and any number of user defined transport vehicles was developed. Three forage harvesting systems were observed using Global Positioning System (GPS) equipment and the collected data used for the TruckSim model validation. The harvest model predicted harvest times within 10% of observed data and yielded similar results to a previously assessed harvest system. Model scenarios were used to explore the effect of differently sized transport vehicles on harvest time and it was found that placing transport vehicles with longer cycle times at the end of the rotation has the potential to reduce harvest time. The TruckSim model can be used to determine the optimal number of transport vehicles and their dispatch order to minimize total harvest time. Ongoing research aims to improve machine state definition accuracy and develop a machinery movement optimization model to maximize forage harvest efficiency and feed quality.