Researchers have developed a new model that can help developers assess corn growth in agricultural voltaic plants. They also proposed using spatiotemporal shade distribution (SSD) to optimize crop yield and energy production.
A research group led by scientists at Purdue University has developed a new model for assessing corn growth in agricultural voltaics and has proposed using a spatiotemporal shadow distribution (SSD) model to optimize crop yield and energy production.
The new method is based on the Agricultural Production Systems Simulator (APSIM) plant model, which is based on a finer temporal resolution, the validity of which is reported to be supported by the literature. The SSD model, which takes into account the shading of the PV panels, was used in combination with radiation data from the National Renewable Energy Laboratory (NREL). This combined data was then calibrated and validated with the results of their field measurements.
The field experiment was conducted on an agrivoltaic farm at Purdue University in West Lafayette, Indiana, USA. There, PV panels were deployed in two configurations: 300 W modules placed next to each other, or 100 W modules arranged in an alternating checkerboard pattern. They all used single-axis trackers and are 6.1 meters tall. The setup was tested between April and October 2020.
“Twelve plots are considered for validation,” the academics said. “Ears of corn from three representative plants from each of these plots were collected by hand. A total of 570 maize plants from the region without PV and 36 maize plants from the region with PV were used in the analysis, respectively. The ears were cleaned, imaged and processed using a DuPont pioneer ear photometer.”
The field measurement showed that the maize yield from the area without PV was 10,955 kg/ha, compared to the yield of 10,182 kg/ha from the PV area. This was in line with the new model, which predicted 10,856 kg/ha for the area without PV and 10,102 kg/ha for the agri-PV field.
The researchers then used the model to test the impact of tracker height, array spacing, panel angle and tracking system activation on yield. They first found that designs that lower tracker height without impeding machine movement should be considered, as overall average corn yield is a weak function of tracker height up to 2.44 m.
“However, the variability from one corn row to another increases as the height of the tracker decreases,” they further explained. “Another interesting finding is that for our PV module sizes, increasing the distance between adjacent PV rows to more than 9.1 m, while keeping the total power constant across the land, does not lead to an increase in corn yield based on the total land area. .”
They also found that anti-tracking (AT) around solar noon produced the most significant increase in corn yield. “However, this 5.6% increase in corn yields is quite modest and must be balanced against a substantial decrease in solar energy,” the group pointed out.
The proposed model was presented in “Optimizing agrivoltaic agriculture of maize through farm-scale experimentation and modeling”, published in Cell reports sustainability. The research group also included academics from Denmark’s Aarhus University.
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