The economics of US row crop production with large-scale autonomous machines

Labor challenges are underpinning large multinational farm machine manufacturers' development of autonomy solutions for their large-scale machine offerings. This study simulates a linear optimization model to examine the economics of large-scale autonomous machines for a rotational maize and soybean farm in the Midwest US. Results support the hypothesis that autonomous machines can be economically viable for farms facing severe labor shortages. However, under current technology and pricing structures, conventional mechanization remains the most profitable option for farms with reliable labor. Critical factors shaping the competitiveness of autonomy include subscription fees, field efficiency, and human supervision requirements. As these factors evolve, farm expansion is likely to emerge as an early pathway where large-scale autonomous machines deliver economic advantages.