Researchers from Technische Hochschule Ingolstadt in Germany have developed a crop selection matrix that is intended to be a decision-making tool for agrivoltaic projects.
The proposed matrix evaluates species-specific responses of 12 major crop types to shading, microclimate changes, and crop growth, while also assessing their water needs, shade tolerance, and space requirements.
It relies on key agronomic and environmental parameters coming from 117 validated research studies on agrivoiltaics encompassing more than 25 countries. The 12 crop types are leafy greens, root vegetables, legumes, cereals, brassicas, oilseed crops, berries, fruit trees, herbs, medicinal plants, mushrooms, and pasture grasses.
The matrix utilizes Global Horizontal Irradiation (GHI) data to determine crop suitability within specific solar resource zones. “While regional variations exist, this matrix offers a standardized framework that can be adapted to specific geographical contexts,” the academics emphasized.
They also explained that crops like berries, fruit trees, and vegetables benefit from the microclimates created by the solar panels, which offer protection from wind and high temperatures, while also reducing water evaporation. Furthermore, these crops need less space and offer a higher economic return per unit area.
The survey showed that herbs, grasses, and legumes are particularly suitable for agrivoltaics in water-scarce, arid and semi-arid regions.
The researchers also highlighted that crops such as cereals, fibre crops, and oilseed crops require more space and are less suitable for the large shadow produced by elevated agrivoltaic systems.
“Since low-space-requiring crops dominate agrivoltaics suitability, the installed PV capacity per hectare is lower compared to conventional large-scale PV farms, making agrivoltaics ideal for small-scale farming communities, decentralized renewable energy projects, and sustainable rural development,” they stated.
Their findings can be found in the study “Crop Selection in Agri-PV: International Review based Strategic Decision-Making Model,” published in Solar Compass. “Future research could further refine the matrix by incorporating real-world case studies, empirical field data, and stakeholder input to enhance its applicability across different agricultural landscapes,” they concluded.
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