Benchmarking of off-road machinery operations with the use of geo-referenced data
type: CreativeWork
description: Past research has revealed that farmers do not have the resources to evaluate the efficiency of their off-road machines and in order for them to do so, relevant data must be collected from those machines. The rise of modern on-board computer systems now allows researchers, farmers and off-road machinery manufacturers to collect data from off-road machines while they complete farm operations. The analysis of off-road machinery related data would allow for the benchmarking of machinery productivity, efficiency, performance and cost. Geo-referenced machinery performance data, provides an opportunity for the analysis of machinery performance in relation to unique spatial aspects of agricultural fields to determine their effects on the operation. The goal of this study was to identify, analyze and benchmark relevant geo-referenced machinery performance data based on selected productivity, efficiency, performance and cost indicators. The methodology was applied to corn planting operations on a farm in east-central Iowa involving a 24-row planter. The methodology was applied to two fields that were selected based on their differences in shape and slope (%). Field one featured a water way which split the field into two right triangles, while field two featured a high average slope (%). Field one, was found to be the more productive and efficient operation compared to field two. Actual field capacity, field efficiency, fuel efficiency and cost were 9.46 ha h-1, 56.3%, 4.27 L ha-1 and $6.54 ha-1 for field one, respectively, compared to field two’s 7.48 ha h-1, 44.5%, 5.01 L ha-1 and $7.84 ha-1. The key factor that contributed to the differences was that the tractor/planter was unproductive for 49% of the time it was in field two, compared to only 11.2% of the time in field one. The large amount of unproductive time reduced the productivity and efficiency of field two and increased the cost. A row-by-row analysis was conducted on the second operation to determine if field slop…
abstract: Past research has revealed that farmers do not have the resources to evaluate the efficiency of their off-road machines and in order for them to do so, relevant data must be collected from those machines. The rise of modern on-board computer systems now allows researchers, farmers and off-road machinery manufacturers to collect data from off-road machines while they complete farm operations. The analysis of off-road machinery related data would allow for the benchmarking of machinery productivity, efficiency, performance and cost. Geo-referenced machinery performance data, provides an opportunity for the analysis of machinery performance in relation to unique spatial aspects of agricultural fields to determine their effects on the operation. The goal of this study was to identify, analyze and benchmark relevant geo-referenced machinery performance data based on selected productivity, efficiency, performance and cost indicators. The methodology was applied to corn planting operations on a farm in east-central Iowa involving a 24-row planter. The methodology was applied to two fields that were selected based on their differences in shape and slope (%). Field one featured a water way which split the field into two right triangles, while field two featured a high average slope (%). Field one, was found to be the more productive and efficient operation compared to field two. Actual field capacity, field efficiency, fuel efficiency and cost were 9.46 ha h-1, 56.3%, 4.27 L ha-1 and $6.54 ha-1 for field one, respectively, compared to field two’s 7.48 ha h-1, 44.5%, 5.01 L ha-1 and $7.84 ha-1. The key factor that contributed to the differences was that the tractor/planter was unproductive for 49% of the time it was in field two, compared to only 11.2% of the time in field one. The large amount of unproductive time reduced the productivity and efficiency of field two and increased the cost. A row-by-row analysis was conducted on the second operation to determine if field slop…
author: Harper, Matthew
language: en
keywords: Benchmarking of machine operations, machinery productivity, efficiency, performance and cost, big data analysis
date published: 2014-09-16T17:25:57Z
date modified: 2026-07-22T22:25:41Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/50732