Optimizing corn agrivoltaic farming through farm-scale experimentation and modeling

Varsha Gupta; Shelby M. Gruss; Davide Cammarano; Sylvie M. Brouder; Peter A. Bermel; Mitchel R. Tuinstra; Margaret W. Gitau; Rakesh Agrawal

Cell Reports Sustainability (Jul 2024)

Optimizing corn agrivoltaic farming through farm-scale experimentation and modeling

Varsha Gupta,
Shelby M. Gruss,
Davide Cammarano,
Sylvie M. Brouder,
Peter A. Bermel,
Mitchel R. Tuinstra,
Margaret W. Gitau,
Rakesh Agrawal

Affiliations

Varsha Gupta: Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA
Shelby M. Gruss: Department of Agronomy, Purdue University, West Lafayette, IN, USA
Davide Cammarano: Department of Agroecology, Aarhus University, Tjele, Denmark
Sylvie M. Brouder: Department of Agronomy, Purdue University, West Lafayette, IN, USA
Peter A. Bermel: Birck Nanotechnology Center, Purdue University, West Lafayette, IN, USA
Mitchel R. Tuinstra: Department of Agronomy, Purdue University, West Lafayette, IN, USA; Corresponding author
Margaret W. Gitau: Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA; Corresponding author
Rakesh Agrawal: Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA; Corresponding author

Journal volume & issue: Vol. 1, no. 7
p. 100148

Abstract

Read online

Summary: To address the limited agrivoltaic research with photovoltaics (PVs) collocated with major row crops, such as corn (Zea mays), we collected extensive corn growth data from neighboring “without-PV” (unshaded) and “with east-west Sun-tracking-PV” regions. The Agricultural Production Systems Simulator (APSIM) plant model calibrated with unshaded region data provided excellent agreement with the experimental PV region corn yield when using hourly light intensity for each plant row computed using spatiotemporal shadow distribution (SSD) between PV panels from our shadow model. The validated APSIM and PV shadow models are then simulated for insights on plant performance and power generation at various PV panel heights, distances between the adjacent PV rows, tracking angles, tracking and anti-tracking during different times of the day and different periods of plant growth, etc. We observed that corn yield is governed by SSD and total radiation, highlighting active control of shadow distribution to optimize crop yield and power production in agrivoltaic farming. Science for society: By the end of the current century, Earth’s population will rise to over 9 billion people. Meeting the food and energy demands of the global population during adverse climate conditions will require sustainable food and energy solutions given the limited land availability. One such solution is agrivoltaics, a practice of co-producing food and energy by installing photovoltaics on agricultural farmland. Through extensive corn growth data, we present a calibrated and validated crop model integrated with an analytical shadow model. Using this model, we observe that the corn yield is determined by spatiotemporal shadow distribution in addition to total radiation. Moreover, we demonstrate that the full Sun data-calibrated crop model predicts crop growth for the PV-shaded region given the shadow profile from the analytical shadow model.

Published in Cell Reports Sustainability

ISSN: 2949-7906 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Geography. Anthropology. Recreation: Environmental sciences; Science: Biology (General): Ecology
Website: https://www.cell.com/cell-reports-sustainability/home

About the journal

Abstract

Keywords