Economically optimal farmer supervision of crop robots
2023
Maritan, Elias | Lowenberg-DeBoer, James | Behrendt, Karl | Franklin, Kit
One of the key issues in regulation of crop robots is the need for human supervision. Economic analysis indicates that autonomous farming potentially reduces agricultural production costs, but such costs may often become higher than conventional when constant on-site human supervision is required by law. However, there are cases where a higher level of crop robot supervision helps maximise profits even if it is not mandated by law, such as when field operations or crop robots inherently require frequent human intervention. The objective of this study is to identify economically optimal levels of farmer supervision of crop robots in the absence of regulation through the HFH-LP optimisation model developed at Harper Adams University, Newport (UK). Four scenarios characterised by different human intervention requirements are developed and compared with two baseline scenarios to identify thresholds at which farm management decisions would change from remote supervision of crop robots to on-site supervision. The findings of this analysis show that the economically optimal farmer supervision of crop robots falls within a range which is substantially lower than the 100% level mandated by jurisdictions such as the EU and California. More specifically, the economically optimal supervision of crop robots falls between 13% and 85% of machine field time across scenarios depending on: (i) the required number of human interventions in a given field operation; (ii) the supervisor's location; and (iii) the number of crop robots being used in that operation. The economic effects of these three factors reveal crucial implications for health and safety regulators and draw attention to crop robot reliability as a priority for researchers, entrepreneurs, and crop robot manufacturers.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS