Benchmarking of off-road machinery operations with the use of geo-referenced data

Benchmarking of machine operations;machinery productivity, efficiency, performance and cost;big data analysis

Harper, Matthew ; Hansen ; Alan C.

关键词: Benchmarking of machine operations;    machinery productivity, efficiency, performance and cost;    big data analysis;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/50732/Matthew_Harper.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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 slope (%) was correlated with energy efficiency. The correlation analysis returned an R2 value of 0.0511, indicating no relationship existed. Engine power was found to vary significantly between certain rows. The average power in the rows was found to be 92 kW with a standard deviation of 33 kW. The average engine speed for fourteen of the seventeen rows was 1426 r min-1, compared to an average of 900 r min-1 for the remaining three rows. It was determined that the machine operator must have reduced the engine throttle when working in three of the rows. The benchmarking methodology was also used to determine the effects of the water way in field one on tractor turning performance. The presence of the water way caused the tractor to make a different shaped turn at the water way edge of the field. The average time for the tractor to complete a turn at the water way edge of the field was found to be 5.8 seconds greater than the opposite side of the field where no water way was present. The extra turning time required at the water way edge of the field increased the total turning time by 13.5%. Some assumptions were made concerning this field to predict field efficiency if the water way did not exist. Field efficiency was predicted to increase from 50.2% to 69.9%, if the water way was not present.. The benchmarking of individual machine operations conducted on a farm could be combined to benchmark the productivity, efficiency, performance and cost of all the machine operations conducted on a farm. This would empower farm managers to budget time and money more accurately for future machine operations by reviewing past benchmarking records. Farm mangers would also be able to evaluate each individual machine and operator on their farm to identify opportunities to improve their overall operation.