The carbon footprint and economic impact of precision agriculture technology on a corn and soybean farm

This study explores the environmental and economic implications of precision agriculture technologies (PATs) to partially fill the void in empirically based estimates in the available published literature. In doing so, the carbon footprint of a Kentucky grain farmer under different production strategies is estimated. A BASE model was optimised without utilising any PATs and compared to models which did incorporate three PATs: sub-meter auto-steer, RTK auto-steer and automatic section control. The four whole farm analysis models were formulated under no-till conditions. These models were used to determine if these PATs (1) increase expected mean net returns and/or (2) enhance the carbon output–input ratio. The results show that all PATs produce improvements in both economic and environmental measures over the BASE model. Specifically, automatic section control gave the greatest economic improvement with a mean net return that was 0.59% over the BASE while RTK provided the greatest environmental enhancement with an improvement of 2.42% over the BASE model. All of the improvements over the BASE scenario can be attributed to the adoption of PAT in the models.