The improvement of economic management in farms has become an important research topic in recent decades as the most dominant feature of current farm management information systems (FMIS). Production cost statistics allow farmers to assess the economic impact of farm activities and compare historical data against previous farm practices or competitors’ activities. Therefore, the availability of reliable cost data is of utmost importance for FMIS, especially data on agricultural machinery usage. Technical sheets, grey literature, and international standards provide estimates of farm operation costs, but they suffer from low accuracy because agricultural machinery is subjected to the high variability of both environmental and working conditions. Based on these considerations, this work aims to develop a novel methodology for cost calculations of field operations harnessing real-world CANBUS data based on the activity-based costing (ABC) approach. The research was conducted on a 198-kW tractor equipped with a CANBUS logger and several implements on which Bluetooth beacons were installed to automatically recognise agricultural operations. The acquired data were processed to identify the daily jobs performed by observing machine position (e.g., field, farm, or road) and operating condition states (e.g., moving, fieldwork, or idling). The ABC approach was applied in two steps: first, cost driver rates were assessed to define capital and non-capital costs; then, the costs of each agricultural operation performed were defined, correlating the cost drivers with the recorded jobs. The results show that fuel and labour costs combined affect 63%–71% of the total cost per hectare for the tested implements. The cost per hectare was found to be highly variable: the biggest gap between the higher and lower values registered with the same implement was 216.48 € ha 1. This methodology could help farmers to make more thoughtful decisions about crop, land, and farm operations management.

CANBUS-enabled activity-based costing for leveraging farm management

Mattetti, Michele;Medici, Marco;Canavari, Maurizio;Varani, Massimiliano
2022

Abstract

The improvement of economic management in farms has become an important research topic in recent decades as the most dominant feature of current farm management information systems (FMIS). Production cost statistics allow farmers to assess the economic impact of farm activities and compare historical data against previous farm practices or competitors’ activities. Therefore, the availability of reliable cost data is of utmost importance for FMIS, especially data on agricultural machinery usage. Technical sheets, grey literature, and international standards provide estimates of farm operation costs, but they suffer from low accuracy because agricultural machinery is subjected to the high variability of both environmental and working conditions. Based on these considerations, this work aims to develop a novel methodology for cost calculations of field operations harnessing real-world CANBUS data based on the activity-based costing (ABC) approach. The research was conducted on a 198-kW tractor equipped with a CANBUS logger and several implements on which Bluetooth beacons were installed to automatically recognise agricultural operations. The acquired data were processed to identify the daily jobs performed by observing machine position (e.g., field, farm, or road) and operating condition states (e.g., moving, fieldwork, or idling). The ABC approach was applied in two steps: first, cost driver rates were assessed to define capital and non-capital costs; then, the costs of each agricultural operation performed were defined, correlating the cost drivers with the recorded jobs. The results show that fuel and labour costs combined affect 63%–71% of the total cost per hectare for the tested implements. The cost per hectare was found to be highly variable: the biggest gap between the higher and lower values registered with the same implement was 216.48 € ha 1. This methodology could help farmers to make more thoughtful decisions about crop, land, and farm operations management.
2022
Mattetti, Michele; Medici, Marco; Canavari, Maurizio; Varani, Massimiliano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/861128
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