Economics of strip cropping with autonomous machines

Abstract

Autonomous machines have the potential to maintain food production and agroecological farming resilience. However, autonomous complex mixed cropping is proving to be an engineering challenge because of differences in plant height and growth pattern. Strip cropping is technically the simplest mixed cropping system, but widespread use is constrained by higher labor requirements in conventional mechanized farms. Researchers have long hypothesized that autonomous machines (i.e., crop robots) might make strip cropping profitable, thereby allowing farmers to gain additional agroecological benefits. To examine this hypothesis, this study modeled ex‐ante scenarios for the Corn Belt of central Indiana, using the experience of the Hands Free Hectare‐Linear Programming (HFH‐LP) optimization model. Results show that per annum return to operator labor, management, and risk‐taking (ROLMRT) was $568/ha and $163/ha higher for the autonomous corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] strip crop farm compared to the whole field sole crop and the conventional strip crop farms, respectively, that were operated by human drivers. The conventional strip cropping practice was found challenging as this cropping system required four times more temporary hired labor than autonomous strip cropping and three times more than whole field sole cropping. Even if autonomous machines need 100% human supervision, the ROLMRT was higher compared to whole field sole cropping. Profitable autonomous strip cropping could restore and improve in‐field biodiversity and ecosystem services through a sustainable techno‐economic and environmental approach that will address the demand for healthier food and promote environmental sustainability.