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

doi:10.1016/j.crsus.2024.100148

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^*

^*Corresponding author af dette arbejde

Institut for Agroøkologi - Klima og Vand

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

2 Citationer (Scopus)

Abstract

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.

Originalsprog	Engelsk
Artikelnummer	100148
Tidsskrift	Cell Reports Sustainability
Vol/bind	1
Nummer	7
DOI	https://doi.org/10.1016/j.crsus.2024.100148
Status	Udgivet - 26 jul. 2024

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title = "Optimizing corn agrivoltaic farming through farm-scale experimentation and modeling",

abstract = "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.",

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author = "Varsha Gupta and Gruss, {Shelby M.} and Davide Cammarano and Brouder, {Sylvie M.} and Bermel, {Peter A.} and Tuinstra, {Mitchel R.} and Gitau, {Margaret W.} and Rakesh Agrawal",

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AU - Gupta, Varsha

AU - Gruss, Shelby M.

AU - Cammarano, Davide

AU - Brouder, Sylvie M.

AU - Bermel, Peter A.

AU - Tuinstra, Mitchel R.

AU - Gitau, Margaret W.

AU - Agrawal, Rakesh

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N2 - 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.

AB - 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.

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KW - agrivoltaics

KW - agrophotovoltaic

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KW - PVA

KW - renewables

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KW - sustainability

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Optimizing corn agrivoltaic farming through farm-scale experimentation and modeling

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