Automated activity-based costing for large tractor fleets: A scalable CANBUS framework for farm economics - Massimiliano Varani; Giovanni Molari; Gianvito Annesi; Michele Mattetti (Smart Agricultural Technology)
The increasing complexity of modern agriculture and the growing demand for sustainability have accelerated the adoption of advanced decision-support systems in farm management. Accurate cost estimation of machinery operations is particularly critical in large-scale farming, where machinery represents a substantial portion of operational expenses. Traditional costing methods often rely on assumptions, leading to inefficiencies. Activity-Based Costing (ABC) offers a more accurate alternative by linking costs to specific operations, but its data-intensive nature has limited practical application. This study presents an improved methodology that combines ABC with automated data collection from CANBUS systems integrated into agricultural tractors. CANBUS technology enables the continuous acquisition of detailed operational data, such as engine load, speed, fuel consumption, and positioning, without manual input. These data are processed to classify tasks and allocate resource use precisely. The methodology was applied to a fleet of nine tractors - ranging from utility to high-power models - on a cooperative farm in Italy over a one-year period. The CANBUS data were integrated with economic parameters like depreciation, maintenance, fuel, and labour costs to estimate job-specific and per-hectare costs. While results showed variations in cost structure and usage patterns across tractor types, the primary contribution of this work lies in demonstrating a scalable, automated ABC framework. This approach reduces reliance on manual reporting, enhances cost transparency, and supports more informed machinery management decisions. It offers a strong foundation for future digital tools aimed at improving the efficiency and sustainability of farm operations, especially in data-rich and mechanized agricultural systems.