Evaluating Photosynthetic Light Response Models for Leaf Photosynthetic Traits in Paddy Rice (Oryza sativa L.) Under Field Conditions

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 (<i>Oryza sativa</i> 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 <i>R²</i>, <i>SSE</i>, and <i>AIC</i> 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 (<i>R_D</i>), light-compensation point (<i>I_comp</i>), maximum net photosynthetic rate (<i>P_Nma</i>_x), and light-saturation point (<i>I_sat</i>). Results also suggest that <i>P_NImax</i> and <i>I_max</i> 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.