Citation
Abstract
Weed management in large-scale solar photovoltaic (LSS-PV) farms has become a great concern to the solar industry due to scarcity of labour and the ever-increasing price of pesticides, which opens up possibilities for integrated farming, also known as agrivoltaics. Improper weed control may have multiple negative impacts such as permanent shading of the module surface, pest housing which damages communication cables, and even bush fires. The shaded PV modules can be heated up to extreme temperatures, causing costly burn-out damage. Critical information on the types of weeds on solar farms, especially in Malaysia, has not been established to support the concept of weed management. Thus, with this study, detailed composition of the weed community was obtained via quadrat sampling between solar PV modules, near ground equipment, near perimeter fencing, and directly underneath the PV modules. Weed-control measures via high-quality weedmat installation under solar PV arrays have been implemented where this approach can be considered effective on solar farms based on the existing PV structure height and equipment constraints plus the increasing cost for labour and agricultural inputs. This work underlines the proposed Agrivoltaic for Large Scale Solar (Agrivoltaic4LSS) program to complement the solar industry in Malaysia towards an agrivoltaic, eco-friendly approach to weed management.
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Official URL or Download Paper: https://www.mdpi.com/2073-4395/12/12/3073
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Agriculture Faculty of Engineering Faculty of Forestry and Environment |
DOI Number: | https://doi.org/10.3390/agronomy12123073 |
Publisher: | Multidisciplinary Digital Publishing Institute |
Keywords: | Weed identification; Agrivoltaic4LSS; Tropical climate; Large-scale solar; Sustainable cities; Agriculture |
Depositing User: | Ms. Nur Faseha Mohd Kadim |
Date Deposited: | 10 Jul 2024 07:48 |
Last Modified: | 10 Jul 2024 07:48 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/agronomy12123073 |
URI: | http://psasir.upm.edu.my/id/eprint/100214 |
Statistic Details: | View Download Statistic |
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