Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
Field Lab Campus Klein-Altendorf, University of Bonn, Rheinbach 53359, Germany.
Plant Physiol. 2022 Jan 20;188(1):301-317. doi: 10.1093/plphys/kiab483.
Photosynthesis acclimates quickly to the fluctuating environment in order to optimize the absorption of sunlight energy, specifically the photosynthetic photon fluence rate (PPFR), to fuel plant growth. The conversion efficiency of intercepted PPFR to photochemical energy (ɛe) and to biomass (ɛc) are critical parameters to describe plant productivity over time. However, they mask the link of instantaneous photochemical energy uptake under specific conditions, that is, the operating efficiency of photosystem II (Fq'/Fm'), and biomass accumulation. Therefore, the identification of energy- and thus resource-efficient genotypes under changing environmental conditions is impeded. We long-term monitored Fq'/Fm' at the canopy level for 21 soybean (Glycine max (L.) Merr.) and maize (Zea mays) genotypes under greenhouse and field conditions using automated chlorophyll fluorescence and spectral scans. Fq'/Fm' derived under incident sunlight during the entire growing season was modeled based on genotypic interactions with different environmental variables. This allowed us to cumulate the photochemical energy uptake and thus estimate ɛe noninvasively. ɛe ranged from 48% to 62%, depending on the genotype, and up to 9% of photochemical energy was transduced into biomass in the most efficient C4 maize genotype. Most strikingly, ɛe correlated with shoot biomass in seven independent experiments under varying conditions with up to r = 0.68. Thus, we estimated biomass production by integrating photosynthetic response to environmental stresses over the growing season and identified energy-efficient genotypes. This has great potential to improve crop growth models and to estimate the productivity of breeding lines or whole ecosystems at any time point using autonomous measuring systems.
光合作用能快速适应不断变化的环境，以优化对阳光能量的吸收，特别是光合光子通量密度（PPFR），从而促进植物生长。截获的 PPFR 转化为光化学能量（ɛe）和生物质（ɛc）的效率是描述植物随时间推移的生产力的关键参数。然而，它们掩盖了特定条件下瞬时光化学能量吸收的联系，即光系统 II（Fq'/Fm'）的运行效率和生物质积累。因此，在不断变化的环境条件下，识别节能和资源高效的基因型受到阻碍。我们使用自动化叶绿素荧光和光谱扫描，在温室和田间条件下，对 21 个大豆（Glycine max (L.) Merr.）和玉米（Zea mays）基因型的冠层水平 Fq'/Fm'进行了长期监测。基于基因型与不同环境变量的相互作用，对整个生长季节的入射阳光下的 Fq'/Fm'进行建模。这使我们能够累积光化学能量吸收，并因此非侵入性地估计 ɛe。根据基因型的不同，ɛe 范围从 48%到 62%不等，在最有效的 C4 玉米基因型中，高达 9%的光化学能量转化为生物质。最引人注目的是，在七个独立的实验中，在不同条件下，ɛe 与地上生物量之间的相关性高达 r=0.68。因此，我们通过整合光合作用对环境胁迫的响应来估计整个生长季节的生物量，并识别节能基因型。这极大地提高了改进作物生长模型的潜力，并使用自主测量系统随时估算育种系或整个生态系统的生产力。
