Vertical farming goes dynamic: optimizing resource use efficiency, product quality, and energy costs - Kaiser, Elias; Kusuma, Paul; Vialet-Chabrand, Silvere; Folta, Kevin; Liu, Ying; Poorter, Hendrik; Woning, Nik; Shrestha, Samikshya; Ciarreta, Aitor; van Brenk, Jordan; Karpe, Margarethe; Ji, Yongran; et al. (Frontiers in Science, 2024)
Vertical farming is considered to be a key enabler for transforming agrifood systems, especially in or nearby urbanized areas. Vertical farming systems (VFS) are advanced indoor cropping systems that allow for highly intensified and standardized plant production. The close control of environmental parameters makes crop production stable and repeatable, ensuring year-round uniform product quality and quantity irrespective of location. However, due to continuous changes in plant physiology and development, as well as frequent changes in electricity prices, the optimum conditions for crop production and its associated costs can change within days or even minutes. This makes it beneficial to dynamically adjust setpoints for light (intensity, spectrum, pattern, and daylength), CO2, temperature, humidity, air flow, and water and nutrient availability. In this review, we highlight the beneficial effects that dynamic growth conditions can have on key plant processes, including improvements in photosynthetic gas exchange, transpiration, organ growth, development, light interception, flowering, and product quality. Our novel findings based on modeling and experimentation demonstrate that a dynamic daily light intensity pattern that responds to frequent changes in electricity prices can save costs without reducing biomass. Further, we argue that a smart, dynamic VFS climate management requires feedback mechanisms: several mobile and immobile sensors could work in combination to continuously monitor the crop, generating data that feeds into crop growth models, which, in turn, generate climate setpoints. In addition, we posit that breeding for the VFS environment is at a very early stage and highlight traits for breeding for this specialized environment. We envision a continuous feedback loop between dynamic crop management, crop monitoring, and trait selection for genotypes that are specialized for these conditions.