College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China.
Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, The Netherlands.
Glob Chang Biol. 2018 Apr;24(4):1685-1707. doi: 10.1111/gcb.13961. Epub 2017 Nov 27.
Leaf photosynthesis of crops acclimates to elevated CO and temperature, but studies quantifying responses of leaf photosynthetic parameters to combined CO and temperature increases under field conditions are scarce. We measured leaf photosynthesis of rice cultivars Changyou 5 and Nanjing 9108 grown in two free-air CO enrichment (FACE) systems, respectively, installed in paddy fields. Each FACE system had four combinations of two levels of CO (ambient and enriched) and two levels of canopy temperature (no warming and warmed by 1.0-2.0°C). Parameters of the C photosynthesis model of Farquhar, von Caemmerer and Berry (the FvCB model), and of a stomatal conductance (g ) model were estimated for the four conditions. Most photosynthetic parameters acclimated to elevated CO , elevated temperature, and their combination. The combination of elevated CO and temperature changed the functional relationships between biochemical parameters and leaf nitrogen content for Changyou 5. The g model significantly underestimated g under the combination of elevated CO and temperature by 19% for Changyou 5 and by 10% for Nanjing 9108 if no acclimation was assumed. However, our further analysis applying the coupled g -FvCB model to an independent, previously published FACE experiment showed that including such an acclimation response of g hardly improved prediction of leaf photosynthesis under the four combinations of CO and temperature. Therefore, the typical procedure that crop models using the FvCB and g models are parameterized from plants grown under current ambient conditions may not result in critical errors in projecting productivity of paddy rice under future global change.
作物叶片光合作用能适应 CO 浓度升高和温度升高,但在田间条件下定量研究叶片光合作用参数对 CO 和温度升高的综合响应的研究很少。我们在两个分别安装在稻田中的自由空气 CO 富集(FACE)系统中测量了两个水稻品种(长优 5 号和南京 9108)的叶片光合作用。每个 FACE 系统有四种 CO 浓度(环境和富集)和两种冠层温度(不加热和加热 1.0-2.0°C)的组合。我们为这四种条件估算了 Farquhar、von Caemmerer 和 Berry(FvCB)模型的光合作用参数和气孔导度(g )模型的参数。大多数光合作用参数适应了 CO 浓度升高、温度升高及其组合。CO 浓度升高和温度升高的组合改变了长优 5 号叶片氮含量的生化参数与功能关系。g 模型在没有适应的情况下,对长优 5 号叶片的 g 低估了 19%,对南京 9108 号叶片的 g 低估了 10%,如果假设 CO 浓度升高和温度升高的组合。然而,我们应用耦合 g-FvCB 模型对一个独立的、以前发表的 FACE 实验的进一步分析表明,如果不考虑 g 的这种适应反应,将 g-FvCB 模型用于预测叶片光合作用,在 CO 和温度的四种组合下几乎不能提高预测精度。因此,使用 FvCB 和 g 模型的作物模型从当前环境条件下生长的植物中进行参数化的典型过程,可能不会导致在未来全球变化下预测水稻生产力的关键错误。