The Effects of Light Spectrum and Intensity, Seeding Density, and Fertilization on Biomass, Morphology, and Resource Use Efficiency in Three Species of

Light is a critical component of indoor plant cultivation, as different wavelengths can influence both the physiology and morphology of plants. Furthermore, fertilization and seeding density can also potentially interact with the light recipe to affect production outcomes. However, maximizing production is an ongoing research topic, and it is often divested from resource use efficiencies. In this study, three species of microgreens-kohlrabi; mustard; and radish-were grown under five light recipes; with and without fertilizer; and at two seeding densities. We found that the different light recipes had significant effects on biomass accumulation. More specifically, we found that Far-Red light was significantly positively associated with biomass accumulation, as well as improvements in height, leaf area, and leaf weight. We also found a less strong but positive correlation with increasing amounts of Green light and biomass. Red light was negatively associated with biomass accumulation, and Blue light showed a concave downward response. We found that fertilizer improved biomass by a factor of 1.60 across species and that using a high seeding density was 37% more spatially productive. Overall, we found that it was primarily the main effects that explained microgreen production variation, and there were very few instances of significant interactions between light recipe, fertilization, and seeding density. To contextualize the cost of producing these microgreens, we also measured resource use efficiencies and found that the cheaper 24-volt LEDs at a high seeding density with fertilizer were the most efficient production environment for biomass. Therefore, this study has shown that, even with a short growing period of only four days, there was a significant influence of light recipe, fertilization, and seeding density that can change morphology, biomass accumulation, and resource input costs.