An economic feasibility assessment of autonomous field machinery in grain crop production

Abstract

A multi-faceted whole farm planning model is developed to compare conventional and autonomous machinery for grain crop production under various benefit, farm size, suitable field day risk aversion, and grain price scenarios. Results suggest that autonomous machinery can be an economically viable alternative to conventional manned machinery if the establishment of intelligent controls is cost effective. An increase in net returns of 24% over operating with conventional machinery is found when including both input savings and a yield increase due to reduced compaction. This study also identifies the break-even investment price for intelligent controls for the safe and reliable commercialization of autonomous machinery. Results indicate that the break-even investment price is highly variable depending on the financial benefits resulting from the deployment of autonomous machinery, farm size, suitable field day risk aversion, and grain prices. The maximum break-even investment price for intelligent, autonomous controls is nearly US$500 000 for the median days suitable for fieldwork when including both input savings and a yield increase due to reduced compaction.

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Notes

  1. 1.

    The annual costs for owning an autonomous machinery was calculated as follows using straight-line depreciation plus opportunity cost of the capital investment: [((total investment − salvage value)/(useful life)) + ((total investment + salvage value) * interest rate)/2].

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Acknowledgements

This research was partially funded by a USDA-CSREES Grant titled “Precision Agriculture: Development and Assessment of Integrated Practices for Kentucky Producers.” Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the US Department of Agriculture.

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Shockley, J.M., Dillon, C.R. & Shearer, S.A. An economic feasibility assessment of autonomous field machinery in grain crop production. Precision Agric 20, 1068–1085 (2019). https://doi.org/10.1007/s11119-019-09638-w

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Keywords

  • Economics
  • Mathematical programming
  • Machinery selection
  • Whole farm planning