| Literature DB >> 35741444 |
Matthieu de Carbonnel1, John M Stormonth-Darling2, Weiqi Liu3, Dmytro Kuziak1, Matthew Alan Jones3.
Abstract
Intensive agriculture is essential to feed increasing populations, yet requires large amounts of pesticide, fertiliser, and water to maintain productivity. One solution to mitigate these issues is the adoption of Vertical Farming Systems (VFS). The self-contained operation of these facilities offers the potential to recycle agricultural inputs, as well as sheltering crops from the effects of climate change. Recent technological advancements in light-emitting diode (LED) lighting technology have enabled VFS to become a commercial reality, although high electrical consumption continues to tarnish the environmental credentials of the industry. In this review, we examine how the inherent use of electricity by VFS can be leveraged to deliver commercial and environmental benefits. We propose that an understanding of plant photobiology can be used to vary VFS energy consumption in coordination with electrical availability from the grid, facilitating demand-side management of energy supplies and promoting crop yield.Entities:
Keywords: chronobiology; circadian; controlled environment agriculture; demand side management
Year: 2022 PMID: 35741444 PMCID: PMC9220163 DOI: 10.3390/biology11060922
Source DB: PubMed Journal: Biology (Basel) ISSN: 2079-7737
Figure 1Vertical farming system activities organised in a life cycle assessment process diagram to highlight the carbon costs in controlled environment agriculture. Groups highlighted in red represent activities with high environmental impact that are addressed in this review.
Figure 2Controlled environment agriculture enables precise control and monitoring of the growth environments. Light properties that can be varied include photoperiod (daylength), intensity, and quality. These factors interact with other variables (including temperature, humidity, and CO2 concentration), which are not considered in this review. Future progress in this field will be accelerated by the improved measurement of photosynthetic performance, crop quality indicators, and nutritional media composition, which could be combined with machine learning to further improve lighting regimes.