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Abstract

Compaction-induced soil degradation is of growing importance as field machinery continues to increase in power and mass. Approaches to managing the impacts of soil compaction include minimisation (reduce load), remediation (tillage) and confinement (control traffic). Integrated ‘swarms’ of low-mass autonomous machinery have recently been suggested as a means of reducing compaction and an alternative to controlled traffic. In this study, combine and potato harvester machinery relationships were used to predict the specifications of potential low-mass harvesters for use in soil compaction modelling. Results suggested that combine harvester gross vehicle mass (GVM) must be less than 6 Mg to keep the modelled soil bulk density below 1.4 Mg m-3. With this constraint, 6-9 small harvesters (~50 kW) would be required to replace one Class 9 (>300 kW) harvester. A fleet of this size would require access to unloading facilities every 2.5-3 min for the modelled yield conditions. For root and tuber harvesting, which results in a high degree of soil disturbance, no low-mass harvester option was found that would avoid compacting the soil to unacceptable limits. Avoiding soil compaction while maintaining acceptable productivity will pose considerable design and logistics challenges for low-mass grain, root and tuber vegetable harvest machinery. The integration of controlled traffic farming (CTF) and medium-capacity autonomous machines (~10-20 Mg GVM for combine harvesters) may be a better solution for both soil compaction and operational logistics than low-mass swarm technology.

Item Type:	Article
Authors/Creators:	McPhee, JE and Antille, DL and Tullberg, JN and Doyle, RB and Boersma, M
Keywords:	autonomous machinery, controlled traffic, harvest, modelling, soil compaction, autonomous vehicles
Journal or Publication Title:	Biosystems Engineering
Publisher:	Academic Press Inc Elsevier Science
ISSN:	1537-5110
DOI / ID Number:	10.1016/j.biosystemseng.2020.05.006
Copyright Information:	Copyright 2020 IAgrE. Published by Elsevier Ltd.
Related URLs:	Google Scholar: Doyle, RB UTAS Profile: Doyle, RB Google Scholar: Boersma, M UTAS Profile: Boersma, M
Item Statistics:	View statistics for this item

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