Claydon Will, Sutton Phoebe, Redmond Ethan J, Vong Gina Y W, Kluczkovski Alana, Thomas Alice, Denby Katherine, Ezer Daphne
Department of Biology, University of York, York, UK.
Vertically Urban, Typhoon House, Leeds, UK.
Quant Plant Biol. 2025 Jul 10;6:e17. doi: 10.1017/qpb.2025.10003. eCollection 2025.
Yield is impacted by the environmental conditions that plants are exposed to. Controlled environmental agriculture provides growers with an opportunity to fine-tune environmental conditions for optimising yield and crop quality. However, space and time constraints will limit the number of experimental conditions that can be tested, which will, in turn, limit the resolution to which environmental conditions can be optimised. Here we present an innovative experimental approach that utilises the existing heterogeneity in light quantity and quality across a vertical farm to evaluate hundreds of environmental conditions concurrently. Using an observational study design, we identify features in light quality that are most predictive of biomass in different kinds of microgreens (kale, radish and sunflower) that may inform future iterations of lighting technology development for vertical farms.
产量会受到植物所接触的环境条件的影响。可控环境农业为种植者提供了一个微调环境条件的机会,以优化产量和作物品质。然而,空间和时间限制会制约可测试的实验条件数量,进而限制环境条件优化的分辨率。在此,我们提出一种创新的实验方法,该方法利用垂直农场中光量和光质的现有异质性,同时评估数百种环境条件。通过观察性研究设计,我们识别出光质中最能预测不同种类微型蔬菜(羽衣甘蓝、萝卜和向日葵)生物量的特征,这些特征可能为垂直农场照明技术开发的未来迭代提供参考。