TY - CONF
T1 - Machinery maneuvering efficiency and perennial crops
T2 - ASABE 2018 Annual International Meeting
AU - Griffel, L. Michael
AU - Vazhnik, Veronika
AU - Hartley, Damon
AU - Hansen, Jason K.
AU - Richard, Tom L.
N1 - Funding Information:
The authors thank the Antares Group and FDC Enterprises for their generous sponsoring support through a cooperative agreement with the U.S. Department of Energy titled: “Enabling Sustainable Landscape Design for Continual Improvement of Operating Bioenergy Supply Systems” (Award Number EE0007088). The research was supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (BETO), under Award No. DE-EE0007088.
Publisher Copyright:
© 2018 American Society of Agricultural and Biological Engineers. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Perennial crops can serve as a feedstock to produce biomaterials and biofuels while enhancing soil and water quality. Perennial crops can provide income from the biomass produced while also delivering valuable ecosystem services. Planting perennial crops benefit the farmer if placed along streams, on steep hillsides, and other environmentally vulnerable areas of a field. However, planting perennial crops in these vulnerable areas may result in complex field shapes which can decrease the machine routing efficiency reducing the economic benefits. Such decrease in logistic machinery efficiency reflects the more time spent on the field and the higher operation cost. This paper analyzed the time series data from switchgrass harvesting operations to show the correlation between field shape descriptors and machinery efficiency. By conducting regression analysis between field area and boundary shape descriptors, and the ratios between working time and total time in the field, we could show the relationship between machinery efficiency and the descriptors. The curb index served as a useful predictor of machinery efficiency and can be used to predict field efficiency based on field geometry, equipment coverage width, and the number of headland passes when empirical data cannot be collected. Such correlation is vital to calculate the cost of perennial crop machinery operations, and thus to estimate the cost-effectiveness of such crops in an integrated landscape design. The established relationship can be used in farm techno-economic analysis, as well as in designing fields that maximize the machinery efficiency given the field shape and in-field obstacles.
AB - Perennial crops can serve as a feedstock to produce biomaterials and biofuels while enhancing soil and water quality. Perennial crops can provide income from the biomass produced while also delivering valuable ecosystem services. Planting perennial crops benefit the farmer if placed along streams, on steep hillsides, and other environmentally vulnerable areas of a field. However, planting perennial crops in these vulnerable areas may result in complex field shapes which can decrease the machine routing efficiency reducing the economic benefits. Such decrease in logistic machinery efficiency reflects the more time spent on the field and the higher operation cost. This paper analyzed the time series data from switchgrass harvesting operations to show the correlation between field shape descriptors and machinery efficiency. By conducting regression analysis between field area and boundary shape descriptors, and the ratios between working time and total time in the field, we could show the relationship between machinery efficiency and the descriptors. The curb index served as a useful predictor of machinery efficiency and can be used to predict field efficiency based on field geometry, equipment coverage width, and the number of headland passes when empirical data cannot be collected. Such correlation is vital to calculate the cost of perennial crop machinery operations, and thus to estimate the cost-effectiveness of such crops in an integrated landscape design. The established relationship can be used in farm techno-economic analysis, as well as in designing fields that maximize the machinery efficiency given the field shape and in-field obstacles.
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U2 - 10.13031/aim.201800440
DO - 10.13031/aim.201800440
M3 - Paper
AN - SCOPUS:85054157158
Y2 - 29 July 2018 through 1 August 2018
ER -