Yao Xinfeng, Sun Huifeng, Zhou Sheng, Li Linyi
Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China.
Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai 201403, China.
Plants (Basel). 2024 Dec 25;14(1):23. doi: 10.3390/plants14010023.
Accurate photosynthetic parameters obtained from photosynthetic light-response curves (LRCs) are crucial for enhancing our comprehension of plant photosynthesis. However, the task of fitting LRCs is still demanding due to diverse variations in LRCs under different environmental conditions, as previous models were evaluated based on a limited number of leaf traits and a small number of LRCs. This study aimed to compare the performance of nine LRC models in fitting a set of 108 LRCs measured from paddy rice ( L.) grown in field across 3 years under different leaf positions, leaf ages, nitrogen levels, irrigation levels, and varieties. The shape of 108 LRCs varies significantly under a range of leaf traits, which can be typed into three leaf light-acclimation types-high-light leaves (HL-1 and HL-2), and low-light leaves (LL). The accuracy of these models was evaluated by (1) LRCs from three acclimation types: HL-1 and HL-2, and LL; and (2) LRCs across three irradiance stages: light-limited, light-saturated, and photoinhibition. Results indicate that the Ye model emerged as the top performance among the nine models, particularly in the photoinhibition stage of LL leaves, with median values of , , and of 0.99, 2.39, and -14.03, respectively. Furthermore, the Ye model produced the most accurate predictions of key photosynthetic parameters, including dark respiration (), light-compensation point (), maximum net photosynthetic rate (), and light-saturation point (). Results also suggest that and were the most appropriate parameters to describe photosynthetic activity at the light-saturation point. These findings have significant implications for improving the accuracy of fitting LRCs, and thus robust predictions of photosynthetic parameters in rice under different environmental conditions.
从光合光响应曲线(LRCs)获得准确的光合参数对于增强我们对植物光合作用的理解至关重要。然而,由于不同环境条件下LRCs存在多种变化,LRCs的拟合任务仍然具有挑战性,因为先前的模型是基于有限数量的叶片性状和少量的LRCs进行评估的。本研究旨在比较九个LRC模型对一组108条LRCs的拟合性能,这些LRCs是在3年田间种植的水稻(L.)上,在不同叶位、叶龄、氮水平、灌溉水平和品种下测得的。108条LRCs的形状在一系列叶片性状下有显著差异,可分为三种叶片光适应类型——高光叶片(HL-1和HL-2)和低光叶片(LL)。通过以下方式评估这些模型的准确性:(1)来自三种适应类型的LRCs:HL-1、HL-2和LL;以及(2)跨越三个辐照阶段的LRCs:光限制、光饱和和光抑制。结果表明,Ye模型在九个模型中表现最佳,特别是在LL叶片的光抑制阶段,其 、 和 的中值分别为0.99、2.39和-14.03。此外,Ye模型对关键光合参数的预测最为准确,包括暗呼吸( )、光补偿点( )、最大净光合速率( )和光饱和点( )。结果还表明, 和 是描述光饱和点光合活性的最合适参数。这些发现对于提高LRCs拟合的准确性具有重要意义,从而能够在不同环境条件下对水稻光合参数进行可靠预测。