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Policies aiding biofuels have supported farm income and rural communities but have also put pressure on food security with questionable benefits related to carbon emissions. Photovoltaics (PV) are poised to become central to the overall energy decarbonization strategy, but because of land requirements they are likely to be developed on farmland, reigniting concerns related to food security. In this work, we study strategies for co-producing food and energy from corn croplands. We find that while traditional PV displaces crops, they can harvest orders of magnitude more energy per unit of land than biofuels. Additionally, systems with elevated PV panels (called PV Aglectric, Agrivoltaics, or Agrophotovoltaics) that allow for crop production underneath them can increase energy production and reduce carbon emissions with minimal impact on crop production. This technology can ease the trade-off between farm income, energy production, crop production, and energy decarbonization. Adoption of PV Aglectric systems may be hindered by high capital costs, but this barrier could be overcome with policy support, especially when crop prices are highly volatile. 

Agrivoltaic systems, which achieve sustainable food and energy co-production (SFE) by installing photovoltaics (PVs) on farmland, offer a climate-resilient solution for meeting ”full Earth” needs while adhering to land limitations. However, limited research on major row crops, such as corn (Zea Mays), constrains the widespread adoption of agrivoltaics. To bridge this research gap, a two-step process was executed. First, extensive corn growth data was collected from neighboring regions, specifically segregating ”with-PV” (shaded) and ”without-PV” (unshaded) areas under real farming conditions. Using data from unshaded areas, the APSIM plant model was calibrated. Subsequently, an analytical shadow model was used to compute the spatiotemporal shadow distribution (SSD) for each row of corn between PV panels. This SSD data helped validate the APSIM model using the experimental corn yield data from shaded areas.