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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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].
Astrand, B., & Baerveldt, A. J. (2002). An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots, 13, 21–35.
Bak, T., & Jakobsen, H. (2004). Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering, 87(2), 125–136.
Blackmore, B. S., & Blackmore, C. P. (2007). People, robots, and systemic decision making. In J. V. Stafford (Ed.), Proceedings of the 6th European conference on precision agriculture, Skiathos, Greece (pp. 433–439). Wageningen Academic Publishers.
Blackmore, B. S., Fountas, S., & Have, H. (2004). System requirements for a small autonomous tractor. Agricultural Engineering International: The CIGR Journal of Scientific Research and Development. Manuscript PM 04 001.
Charnes, A., & Cooper, W. W. (1959). Chance constrained programming. Management Science, 6, 73–79.
Danok, A. B., McCarl, B. A., & White, T. K. (1980). Machinery selection modeling: Incorporation of weather variability. American Journal of Agricultural Economics, 62(4), 700–709.
Dillon, C. R. (1999). Production practice alternatives for income and suitable field day risk management. Journal of Agricultural and Applied Economics, 31(2), 247–261.
Goense, D. (2005). The economics of autonomous vehicles in agriculture. In Presented at the ASAE annual international meeting, Tampa, FL. Paper Number 051056.
Gottschalk, R., Burgos-Artizzu, X. P., & Ribeiro, A. (2009). Development of a small agricultural field inspection vehicle. In Proceedings of the 7th European conference on precision agriculture, Wageningen, Netherlands, July 6–8 (pp. 877–884).
Griepentrog, H. W., Andersen, N. A., Andersen, J. C., Blacke, M., Heinemann, O., Madsen, T. E., Nielsen, J., Pedersen, S. M., Ravn, O., & Wulfsohn, D. (2009). Safe and reliable: Further development of a field robot. In Proceedings of the 7th European conference on precision agriculture, Wageningen, Netherlands, July 6–8 (pp. 857–866).
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.
Laughlin, D. H., & Spurlock, S. R. (2007). Mississippi State Budget Generator v6.0.
Luck, J. D., Sharda, A., Pitla, S. K., Fulton, J. P., & Shearer, S. A. (2011). A case study concerning the effects of controller response and turning movement on application rate uniformity with a self-propelled sprayer. Transactions of the ASABE, 54(2), 423–431.
Marchant, J. A., Hague, T., & Tillett, N. D. (1997). Row-following accuracy of an autonomous vision-guided agricultural vehicle. Computers and Electronics in Agriculture, 16, 165–175.
Murdock, L. W., & James, J. (2008). Compaction, tillage method, and subsoiling effects on crop production. University of Kentucky Cooperative Extension Service Bulletin: AGR-197.
Pedersen, S. M., Fountas, S., & Blackmore, S. (2007). Economic potential of robots for high value crops and landscape treatment. In J. V. Stafford (Ed.), Proceedings of the 6th European conference on precision agriculture, Skiathos, Greece (pp. 457–464). Wageningen Academic Publishers.
Pedersen, S. M., Fountas, S., Have, H., & Blackmore, B. S. (2006). Agricultural robots—System analysis and economic feasibility. Precision Agriculture, 7, 295–308.
Pierce, J. S. (2018). Kentucky farm business management program: Annual summary data 2017. Lexington, KY: University of Kentucky Cooperative Extension Service.
Pitla, S. K., Luck, J. D., & Shearer, S.A. (2010a). Low cost obstacle detection sensor array for unmanned agricultural vehicles. In Presented at the 2010 ASABE annual international meeting, Pittsburgh, PA Paper Number 1008702.
Pitla, S. K., Luck, J. D., & Shearer, S.A. (2010b). Multi-robot system control architecture (MRSCA) for agricultural production. In Presented at the 2010 ASABE annual international meeting, Pittsburgh, PA. Paper Number 1008702.
Ruckelshausen, A., Biber, P., Dorna, M., Gremmes, H., Klose, R., Linz, A., Rahe, R., Resch, R., Thiel, M., Trautz, D., & Weiss, U. (2009). BoniRob: An autonomous field robot platform for individual plant phenotyping. In Proceedings of the 7th European conference on precision agriculture, Wageningen, Netherlands, July 6–8 (pp. 841–847).
Schieffer, J., & Dillon, C. R. (2015). The economic and environmental impacts of precision agriculture and interactions with agro-environmental policy. Precision Agriculture, 16(1), 46–61.
Shockley, J. M., & Dillon, C. R. (2018). An economic feasibility assessment for adoption of autonomous field machinery in row crop production. In Selected Paper prepared for presentation at the 2018 international conference on precision agriculture, Montreal, QC, June 24–26.
Shockley, J. M., Dillon, C. R., & Stombaugh, T. (2011). A whole farm analysis of the influence of auto-steer navigation on net returns, risk, and production practices. Journal of Agricultural and Applied Economics, 43(1), 57–75.
Shockley, J. M., Dillon, C. R., Stombaugh, T., & Shearer, S. (2012). Whole farm analysis of automatic section control for agricultural machinery. Precision Agriculture, 13(4), 411–420.
Shockley, J. M., & Mark, T. B. (2017). AEC-101: Days suitable for fieldwork in Kentucky. University of Kentucky Cooperative Extension Service. www.uky.edu/Ag/AgEcon/pubs/extSFW32.pdf.
University of Kentucky Cooperative Extension Service Bulletins, 2008. AGR1, AGR129, AGR130, AGR132, ID139 Bulletins. http://dept.ca.uky.edu/agc/pub_area.asp?area=ANR.
van Henten, E. J., van Asselt, C. J., Bakker, T., Blaauw, S. K., Govers, M. H. A. M., Hofstee, J. W., Jansen, R. M. C., Nieuwenhuizen, A. T., Speetjens, S. L., Stigter, J. D., van Straten, G., & van Willigenburg, L. G. (2009). WURking: A small sized autonomous robot for the farm of the future. In Proceedings of the 7th European conference on precision agriculture, Wageningen, Netherlands, July 6–8 (pp. 833–840).
Vougioukas, S. (2007). Path tracking control for autonomous tractors with reactive obstacle avoidance based on evidence grids. In J. V. Stafford (Ed.), Proceedings of the 6th European Conference on Precision Agriculture, Skiathos, Greece (pp. 483–490). Wageningen Academic Publishers.
Vougioukas, S. (2009). A framework for motion coordination of small teams of agricultural robots. In Proceedings of the 7th European conference on precision agriculture, Wageningen, Netherlands, July 6–8 (pp. 585–593).
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
- Mathematical programming
- Machinery selection
- Whole farm planning