# Triose phosphate utilization in leaves is modulated by whole-plant sink-source ratios and nitrogen budgets in rice

Zhenxiang Zhou, Zichang Zhang, Peter E L van der Putten, Denis Fabre, Michael Dingkuhn, Paul C Struik, Xinyou Yin

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Zhenxiang Zhou, Zichang Zhang, Peter E L van der Putten, Denis Fabre, Michael Dingkuhn, et al. Triose phosphate utilization in leaves is modulated by whole-plant sink-source ratios and nitrogen budgets in rice. Journal of Experimental Botany, 2023, 74 (21), pp.6692 - 6707. 10.1093/jxb/erad329. hal-04490237

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RESEARCH PAPER

Triose phosphate utilization in leaves is modulated by whole-plant sink-source ratios and nitrogen budgets in rice

Zhenxiang Zhou¹, ID, Zichang Zhang¹,², Peter E. L. van der Putten¹, Denis Fabre³,⁴ ID, Michael Dingkuhn³,⁴ ID, Paul C. Struik¹, ID and Xinyou Yin¹,*, ID

¹ Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, PO Box 430, 6700 AK Wageningen, The Netherlands

² Institute of Plant Protection, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China

³ CIRAD, UMR AGAP Institut, F-34398 Montpellier, France

⁴ UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France

* Correspondence: xinyou.yin@wur.nl

Received 3 June 2023; Editorial decision 18 August 2023; Accepted 25 August 2023

Editor: Isabel Abreu, Universidade Nova de Lisboa, Portugal

Abstract

Triose phosphate utilization (TPU) is a biochemical process indicating carbon sink-source (im)balance within leaves. When TPU limits leaf photosynthesis, photorespiration-associated amino acid exports probably provide an additional carbon outlet and increase leaf CO₂ uptake. However, whether TPU is modulated by whole-plant sink-source relations and nitrogen (N) budgets remains unclear. We address this question by model analyses of gas-exchange data measured on leaves at three growth stages of rice plants grown at two N levels. Sink-source ratio was manipulated by panicle pruning, by using yellower-leaf variant genotypes, and by measuring photosynthesis on adaxial and abaxial leaf sides. Across all these treatments, higher leaf N content resulted in the occurrence of TPU limitation at lower intercellular CO₂ concentrations. Photorespiration-associated amino acid export was greater in high-N leaves, but was smaller in yellower-leaf genotypes, panicle-pruned plants, and for abaxial measurement. The feedback inhibition of panicle pruning on rates of TPU was not always observed, presumably because panicle pruning blocked N remobilization from leaves to grains and the increased leaf N content masked feedback inhibition. The leaf-level TPU limitation was thus modulated by whole-plant sink-source relations and N budgets during rice grain filling, suggesting a close link between within-leaf and whole-plant sink limitations.

**Keywords:** Adaxial versus abaxial measurement, *Oryza sativa*, panicle pruning, triose phosphate utilization, photorespiration-associated nitrogen assimilation, sink limitation, yellower-leaf modification.

Introduction

CO₂ response curves of leaf photosynthesis as obtained from gas exchange analysis are typically described by the canonical Farquhar–von Caemmerer–Berry (FvCB) biochemical photosynthesis model (Farquhar et al., 1980). This model predicts

that leaf photosynthetic rates under the current environmental conditions are determined by two main parameters, Rubisco carboxylation capacity (V~cmax~) and maximum linear electron transport rate (J~max~). The transition from Rubisco limitation to
electron-transport limitation occurs around a leaf internal CO2 level when the equivalent ambient-air CO2 concentration (Cα) is about 400 µmol mol-1 (e.g. Mathan et al., 2021). However, the internal CO2 inside a leaf can attain a high level at which leaf photosynthesis is limited by a third parameter, Tp, the rate of triose phosphate utilization (TPU). Until now, compared with the first two biochemical components (i.e. Vcmax and Jmax), Tp has received less attention since observable TPU limitations only occur occasionally and are highly variable, depending on species, genotype, growth conditions, and measurement conditions (e.g. Kumarathunge et al., 2019). Also, the TPU-limited condition is observed only temporarily, because it can be removed quickly as other parameters like Vcmax and Jmax may be regulated to a level where Tp is no longer ‘apparently’ limiting (McClain et al., 2023). Nevertheless, with the increase of atmospheric CO2 (to roughly 600 µmol mol-1; Lombardozzi et al., 2018), TPU limitation will probably become increasingly important for predicting photosynthesis and yield.

TPU refers to the rate at which triose phosphates exit from the photosynthetic Calvin–Benson–Bassham (CBB) cycle and are used as sugar precursors for processes like the synthesis of sucrose and starch. As triose phosphates are phosphorylated carbon, any TPU requires the returns of inorganic phosphate (Pi) to the chloroplast, since the quantity of phosphate in the chloroplast is finite and under tight homeostasis (McClain and Sharkey, 2019). A limitation of TPU on photosynthesis is triggered when carbon exports from (with the accompanying Pi import to) the cycle cannot keep pace with carbon fixation, which is in essence a local sink–source disequilibrium. TPU limitation causes unresponsiveness to CO2 (Sharkey, 1985) or sometimes reversed sensitivity of photosynthesis to increasing CO2 (e.g. von Caemmerer and Farquhar, 1981; Harley and Sharkey, 1991). The TPU limitation is considered to be a biochemical mechanism for sink limitation on photosynthesis, expressed at a sub-foliar scale (Sharkey, 2019). This differs from the sink (panicles) limitation on source (leaves) activity agronomists commonly define at the whole-plant or crop scale. While sink limitations at sub-foliar and whole-plant scales are not necessarily independent (Yin et al., 2022), few studies have been conducted on their connections.

Agronomists commonly manipulate sink–source ratios at the whole-plant scale by pruning leaves or panicles during the grain filling process, which modifies the transport of assimilates between source and sink organs (Li et al., 2017; He et al., 2019). Many reports (Rossi et al., 2015; Dingkuhn et al., 2020; Fabre et al., 2020) have also demonstrated that genotypes with larger crop carbon sink capacity can benefit more from future CO2-rich climate. Conversely, a smaller sink (e.g. small-panicle genotypes, or plants with panicles pruned) reduces phloem loading, forcing assimilate accumulation in leaves or stems that may exert a feedback inhibition on leaf photosynthetic source activity (Burnett et al., 2016; White et al., 2016), and even on TPU (Fabre et al., 2019).

Carbon metabolism generally interacts with nitrogen (N) assimilation. In fact, Harley & Sharkey (1991) hypothesized that the reversed sensitivity of photosynthesis to increasing CO2 under TPU limitation resulted from diverting a fraction of N-containing glycine from the photorespiratory pathway and this glycine is used elsewhere for other amino acid or protein synthesis. Glycine is derived from the photorespiratory glycolate carbon; normally 25% of the glycolate carbon is lost as CO2 as a result of glycine decarboxylation, and the remaining 75% is recycled to glycerate and further to 3-phosphoglycerate to rejoin the CBB cycle (Supplementary Fig. S1). With the exit of glycine, the Pi normally used in converting glycerate to 3-phosphoglycerate is made available for phosphorylation, thereby stimulating photosynthesis (Harley and Sharkey, 1991). Busch et al. (2018) extended this hypothesis by considering the exit of both glycine and serine from the photorespiratory pathway. The export of N from the photorespiratory pathway requires de novo N assimilation and amino acid synthesis (Supplementary Fig. S1). They proposed that a large proportion of N assimilation in leaves accomplished via the photorespiratory pathway is innately linked with TPU. The exported amino acids represent an additional sink for carbon and decrease Pi consumption for phosphorylating glycerate to 3-phosphoglycerate, thereby explaining the increased photosynthetic rate with decreasing CO2 levels (with increasing photorespiration) within the TPU-limited range (Busch et al., 2018; Yin et al., 2021). The extent of increase in photosynthesis depends on the proportion of glycolate carbon exported from the photorespiratory pathway. It is conceivable that the proportion of amino acid carbon export may be associated with the availability of NO3- for N assimilation. Crop N assimilation occurs throughout the life cycle, but varies with NO3- availability and leaf N content. We hypothesize that values of a parameter related to TPU limitation (i.e. the fraction of the glycolate carbon exported from the photorespiratory pathway) increase with leaf N content, and thus vary among growth stages and N treatments.

In this study, we aim to (i) quantify how TPU-limited photosynthetic carbon uptake is affected by nitrogen assimilation via the photorespiratory pathway; and (ii) analyse how the local sink–source mechanisms of TPU limitation within leaves are regulated by the whole-plant physiological source and sink relationships. We address these aims in the context of our recent effort to examine the impact of leaf-colour modification on photosynthesis, using rice genotypes of different leaf colour based on our previous finding that genotypic leaf yellowness affects leaf photosynthetic rate via several mechanisms including altered leaf morphology (Zhou et al., 2023). As adaxial and abaxial photosynthetic rates are known to differ (e.g. Soares et al., 2008), gas exchange measurements with the local illumination on either leaf side may result in a varied leaf-scale source activity but with a constant sink demand. Thus, in our experimental set-up, we use three means to vary carbon
sink-source ratios (genotypic leaf-colour variants, panicle pruning, and adaxial versus abaxial illumination while measuring gas exchange), and two means to vary nitrogen status (N treatments and plant developmental stages). In addition, we use 21% O₂ versus 2% O₂ conditions to alter photorespiration, likely modifying the amount of glycine and serine export. We hoped to obtain information on how TPU limitation is related to photorespiration-associated N assimilation and affected by altered sink-source ratios within the leaf and at the whole-plant scale.

## Materials and methods

### Plant material and growth conditions

Rice (*Oryza sativa* L.) materials were based on two background genotypes: japonica type cv. Wuyunjing 3 (WYJ) and early indica type cv. Zhefu 802 (ZF). Both were modified by radiation mutagenesis with ⁶⁰Co γ-rays, and the yellower-leaf variants were identified from a larger population of phenotypes. These genotypes showed stability of the lines over generations (Zhou *et al.*, 2023). Hereafter, yellower genotypes are denoted as Y and the wild type as control (C).

Two experiments were conducted in a climate-controlled glasshouse in Wageningen, the Netherlands in 2019 and 2022. The growth conditions were the same as described in our earlier study (Zhou *et al.*, 2023): incident global radiation outside the greenhouse was kept within 400–500 W m⁻² (resulting in a photosynthetic photon flux density measured at plant height of ~500 µmol m⁻² s⁻¹), temperature was set at 26 °C for the 12-h light period and at 23 °C for the 12-h dark period, the CO₂ level was about 400 µmol mol⁻¹, and the relative humidity was 65–75%. The 2019 experiment was to examine if genotypes differ in the extent of photosynthetic differences between adaxial and abaxial illumination.

In the 2022 experiment, nitrogen supply and panicle pruning treatments were added as factors. Nitrogen was applied as urea at two levels: N1 (in total 0.7 g urea per pot) and N2 (in total 1.4 g urea per pot). All pots were evenly divided into four blocks (corresponding to four experimental replicates), and each block contained 64 pots, representing all combinations of four genotypes, two nitrogen levels, and two pruning levels. The four pots per treatment combination were used for measurements at three developmental stages (see below), with one pot as reserve in case of plant damage. Plants to be pruned were randomly selected and pre-labelled, and panicles of these plants were pruned at the moment when the first panicle of the plant had emerged from the flag-leaf sheath. This operation lasted a week to ensure that no new heads were produced from any culms.

### Leaf photosynthesis measurements

Pre-labelled and fully expanded main-stem leaves in each experimental treatment per replicate were measured using an open-path gas exchange system integrated with a fluorescence chamber head (LI-COR 6800; LI-COR Inc., Lincoln, NE, USA) to simultaneously obtain gas exchange and chlorophyll fluorescence parameters. All measurements were carried out at a leaf temperature of 25 °C and a vapour pressure difference of 1.0–1.6 kPa between the leaf and air outside of the leaf, with a flow rate of 400 µmol s⁻¹.

For both 2019 and 2022 experiments, measurements were conducted on the same leaf segment at both adaxial and abaxial sides. It should be noted that measurements on 'adaxial' or 'abaxial' sides always integrated the gas exchange occurring on both sides, as both sides were exposed to the chamber air. However, the light was only received by the side

that faced the light source. Strictly speaking, it was not the gas exchange measurement but the light orientation that varied, causing inverted light gradients through the leaf.

For the 2019 experiment, measurements were conducted only at the tillering stage. Light and CO₂ response curves were measured at the same position at both adaxial and abaxial sides of the leaves. The curves for net photosynthetic rate (A) response to incident irradiance (Iₘc) were obtained with Iₘc in a decreasing series of 2000, 1500, 1000, 500, 280, 150, 100, 80, and 50 µmol m⁻² s⁻¹ (6–8 min per step), while maintaining ambient CO₂ level (Cₐ) at 400 µmol mol⁻¹. The CO₂ response curves were measured at Iₘc of 1000 µmol m⁻² s⁻¹, with the Cₐ steps of 400, 250, 150, 80, 50, 400, 400, 400, 650, 1000, and 1500 µmol mol⁻¹ (3–5 min per step; note that using the three repeated 400 µmol mol⁻¹ steps was merely to re-adapt leaves, and the data from these three points were excluded in the analysis). Both curves were measured at ambient O₂ (21%) level. To estimate day respiration (R₉d) and establish a calibration factor (s) that converts chlorophyll fluorescence-based electron transport efficiency of photosystem II (PSII) into linear electron transport rate (see Yin *et al.*, 2009), we also conducted half of the light response curve (with Iₘc being 280, 150, 100, 80, and 50 µmol m⁻² s⁻¹) under non-photorespiratory conditions (2% O₂ combined with Cₐ at 1000 µmol mol⁻¹). These low light levels were applied to ensure that data for calibration were within the range where A is limited by electron transport. The low O₂ level was realized by using a cylinder containing a gas mixture of 2% O₂ and 98% N₂.

For the 2022 experiment, photosynthesis was measured on both adaxial and abaxial leaf surfaces at three stages: tillering stage (TS), flowering stage (FS), and grain-filling (~15 days after flowering (DAF)). The panicle-pruned plants were only measured at grain filling because these plants were supposed to function the same as the non-pruned plants at tillering and flowering. As this experiment was meant to examine the TPU limitation, only CO₂ response curves (where A is likely limited by TPU) were measured at Iₘc of 1500 µmol m⁻² s⁻¹ under both 21% and 2% O₂ conditions, with the Cₐ in an increasing series: 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, and 1800 µmol mol⁻¹. These Cₐ levels were chosen to ensure that part of the curve could reach the TPU-limited range. As with the 2019 experiment, we additionally measured the light-response curve with Iₘc being 300, 150, 100, 80, and 40 µmol m⁻² s⁻¹ under non-photorespiratory conditions to estimate R₉d and s.

For each irradiance or CO₂ step in both experiments, F'_s (the steady-state fluorescence) was recorded after A reached the steady state. The maximum fluorescence (F'_m) was determined using a three-phase flash method (Loriaux *et al.*, 2013): each phase went through a duration of 300 ms, and flash intensity of 6500 µmol m⁻² s⁻¹ in the second phase was attenuated by 40%. The apparent operating photochemical efficiency of PSII was assessed from chlorophyll fluorescence measurements: Φ₂=1−F'_s/F'_m (Genty *et al.*, 1989).

All gas exchange data were corrected for any small basal leakage of CO₂ into and out of the leaf cuvette, based on measurements on boiled leaves across the CO₂ levels, and intercellular CO₂ levels (Cᵢ) were then re-calculated.

### Leaf SPAD and nitrogen content

All leaf segments used for measuring photosynthesis curves were cut out and used immediately to measure the leaf area with a LI-3100 area meter (LI-COR) and the values for SPAD indicating chlorophyll content (SPAD-502, Minolta Camera Co., Japan). SPAD was measured at both adaxial and abaxial sides of these leaf segments. Leaf materials were then oven-dried at 70 °C for 48 h to constant weight. Specific leaf area (SLA, m² kg⁻¹) was calculated as the leaf area to dry leaf mass ratio. Each leaf segment was ground into powder in a 2-ml centrifuge tube, which was used to measure the N concentration by an element analyser based on the micro-Dumas combustion method. Specific leaf nitrogen (SLN, g N m⁻²) was then calculated.
## Plant growth measurements

At grain-filling stage in the 2022 experiment, the aboveground parts were sampled and separated. Dry weight of each part was determined after oven drying at 75 °C for 72 h to constant weight. The leaf samples were ground into powder, which was then assessed for nitrogen concentration with a Kjeldahl apparatus (Kjeltec 8400, Foss Corp., Germany). Total leaf-nitrogen per pot was calculated by leaf nitrogen concentration multiplied by total leaf dry weight. We counted the fertile spikelet number for each culm, and then measured the flag leaf area (just after photosynthesis measurement) and total leaf area by a LI-3100 area meter (LI-COR). Following Fabre et al. (2020), the ratio of flag leaf area (source) to the fertile spikelet number of the panicle (sink) on the culm was used as an indicator of the single-culm sink-source ratio, while total spikelet number divided by total leaf area of the whole plant was used as an indicator of the whole-plant sink-source ratio.

## Estimating photosynthetic parameters

We estimated parameters of the FvCB model (Farquhar et al., 1980), which expresses net photosynthetic rate (A) as the minimum of the Rubisco carboxylation-limited rate ($A_c$), electron-transport limited rate ($A_j$), and the TPU-limited rate ($A_p$):

$$A = \min(A_c, A_j, A_p) \quad (1)$$

For $A_c$:

$$A_c = \frac{(C_c - \Gamma_*) V_{cmax}}{C_c + K_{mC} (1 + O/K_{mO})} - R_d \quad (2)$$

where $C_c$ and $O$ are the chloroplast partial pressures of CO₂ and O₂, respectively, $V_{cmax}$ is the maximum rate of Rubisco activity for carboxylation, and $K_{mC}$ and $K_{mO}$ are Michaelis-Menten constants of Rubisco for CO₂ and O₂, respectively. $\Gamma_*$ is the CO₂ compensation point in the absence of day respiration ($R_d$), described by: $\Gamma_*=0.5O/S_{c/o}$, where $S_{c/o}$ is the relative CO₂/O₂ specificity factor for Rubisco. Values of these Rubisco parameters vary significantly, depending on techniques used to measure them; here, we used the representative values of Rubisco parameters measured in vitro at 25 °C by Cousins et al. (2010) for wheat: i.e. 291 µbar for $K_{mC}$, 194 mbar for $K_{mO}$, and 3.022 mbar µbar⁻¹ for $S_{c/o}$, given that values of these Rubisco parameters are believed to be conserved among C₃ species (von Caemmerer, 2000).

For $A_j$:

$$A_j = \frac{(C_c - \Gamma_*)J}{4(C_c + 2\Gamma_*)} - R_d \quad (3a)$$

where J is the potential linear electron transport rate supporting the CBB cycle and the photorespiratory cycle. J can be calculated using the calibration factor s, incident irradiance ($I_{inc}$), and fluorescence-based photochemical efficiency of PSII ($\Phi_2$) as: $J = sI_{inc}\Phi_2$, where parameters s and $R_d$ can be estimated from the slope and intercept of a linear plot of $A_j$ against ($I_{inc}\Phi_2/4$) measured under non-photorespiratory conditions (Yin et al., 2009). The calculated J can be fitted according to:

$$J = \frac{\kappa_{2LL} I_{inc} + J_{max} - \sqrt{(\kappa_{2LL} I_{inc} + J_{max})^2 - 4\theta J_{max} \kappa_{2LL} I_{inc}}}{2\theta} \quad (3b)$$

where $J_{max}$ is the maximum value of J under saturated light; $\kappa_{2LL}$ represents the conversion efficiency of incident light into J at strictly limiting

light; and $\theta$ is a dimensionless convexity factor for the response of J to $I_{inc}$, and here a common value of 0.76 for $\theta$ was adopted for all rice genotypes from Zhou et al. (2023).

For $A_p$, the widely used algorithm (Harley and Sharkey, 1991; von Caemmerer, 2000) assumes that glycine is taken out from the photorespiratory pathway. However, this algorithm does not consider the required change of the CO₂ compensation point, as a result of the glycine export, to $(1-\alpha_G)\Gamma_*(\text{Busch et al., 2018}; \text{Yin et al., 2021}, \text{also see Supplementary Fig. S1}; \text{where } \alpha_G \text{ is the proportion of glycolate carbon exported from the photorespiratory pathway in the form of glycine}). Model fitting results of Busch et al. (2018) suggested that the proportion of glycolate carbon exported as glycine is lower than the proportion exported as serine. Isotope-labelling measurements (Abadie et al., 2016; Fu et al., 2022) more convincingly confirmed little export in the form of glycine. As serine export causes no change in the CO₂ compensation point, here for the purpose of simplicity, we assumed only the serine export, for which the model becomes (Yin et al., 2021; see their Equation 17b):

$$A_p = \frac{(C_c - \Gamma_*) (3T_p)}{C_c - (1 + 4\alpha_S) \Gamma_*} - R_d \quad (4)$$

where $\alpha_S$ is the proportion of glycolate carbon exported from photorespiratory pathway in the form of serine (with $0 \le \alpha_S \le 0.75$). This guarantees the same term in the numerator, $(C_c-\Gamma_*)$, which is consistent with Rubisco- or electron transport-limited forms. Such consistency simplifies the modelling algorithms for the next steps of analysis (Equations 5, 6). The simple model in Equation 4 also generates the TPU-limited rate $A_p$ similar to the full model with both glycine and serine export if the total fraction of glycolate carbon export remains the same (Yin et al., 2021). Note that the coefficient in front of the term for the proportion of glycolate carbon export in the denominator of Equation 4 is 4, whereas this is 3 in the commonly used old equation assuming the glycine exit (von Caemmerer, 2000; Ellsworth et al., 2015; Busch and Sage, 2017; Kumarathunge et al., 2019; also see Supplementary Fig. S1). As a result, the old equation, when applied to fit gas-exchange data, overestimates the glycolate carbon export fraction by a factor of 4/3 (Yin et al. 2021).

The method of Harley et al. (1992) was first applied to examine whether mesophyll conductance $g_m$ varied with intercellular CO₂ level ($C_i$) or $I_{inc}$, and we found that $g_m$ is variable and declines with increasing $C_i$ or with decreasing $I_{inc}$, with $g_m=0$ as $I_{inc}$ approaches to zero (results not shown). To describe this pattern of variable $g_m$, we used an equation of Yin et al. (2009):

$$g_m = \frac{\delta(A + R_d)}{(C_c - \Gamma_*)} \quad (5)$$

where parameter $\delta$ represents the carboxylation resistance to mesophyll resistance ratio (Yin et al., 2020). Then, this Equation 5 was combined with Equations 2, 3a, and 4, and $C_c$ was replaced by $(C_i-A/g_m)$ to solve for A (Yin et al., 2020):

$$A = \frac{(-b \pm \sqrt{b^2 - 4ac})}{2a} \quad (6)$$

where

$$a = x_2 + \Gamma_* + \delta(C_i + x_2)$$

$$b = -(x_2 + \Gamma_*) (x_1 - R_d) - \delta(C_i + x_2) (x_1 - R_d) - \delta[x_1(C_i - \Gamma_*) - R_d(C_i + x_2)]$$
$$c = \delta(x_1 - R_d)[x_1(C_i - \Gamma_*) - R_d(C_i + x_2)]$$

where

$$\text{For } A_c \text{ part} \quad \begin{cases} x_1 = V_{cmax} \\ x_2 = K_{mC} (1 + O/K_{mO}) \end{cases}$$

$$\text{For } A_j \text{ part} \quad \begin{cases} x_1 = J/4 \\ x_2 = 2\Gamma_* \end{cases}$$

$$\text{For } A_p \text{ part} \quad \begin{cases} x_1 = 3T_p \\ x_2 = -(1 + 4\alpha_s)\Gamma_* \end{cases}$$

Note when calculating A in Equation 6, the minus sign in front of the $\sqrt{b^2 - 4ac}$ term was applied for either the $A_c$- or $A_j$-limited part while the + sign was required for $A_p$-limited part (see Yin et al., 2020).

For analysing the 2019 data where light-response curves were measured, we first estimated $J_{max}$ by fitting equation 3b to data points of light response of J derived from chlorophyll fluorescence parameters $\Phi_2$ (i.e. $J=sI_{inc}\Phi_2$). Then photosynthetic parameters $\delta$, $V_{cmax}$, $T_p$, and $\alpha_s$ can be estimated simultaneously by fitting combined Equations 1, 3b, and 6 to all CO₂ exchange data from both light- and CO₂-response curves. For data from 2022, as only CO₂ response curves were measured yet starting with $C_a$ from 400 µmol mol⁻¹ onwards that only covered $A_j$- and $A_p$-limited parts, we thus combined $J=sI_{inc}\Phi_2$ and $A_j$ and $A_p$ parts of Equation 6 to estimate parameters $\delta$, $T_p$, and $\alpha_s$ simultaneously.

Once photosynthetic parameters were estimated, the transition point from $A_j$- to $A_p$-limited rates can be solved. We also estimated the transition point by solving the second-order polynomial regression equations that were fitted to $A_j$ and $A_p$ ranges, respectively, of $A-C_i$ curves. The estimated threshold $C_i$ was highly consistent (see Results); thus, we used the polynomial-based values for showing the transition.

## Statistical analyses and curve fitting

Simple linear regressions were conducted using Microsoft Excel. Non-linear regressions were performed using the Gauss method in PROC NLIN of SAS (SAS Institute Inc., Cary, NC, USA). An analysis of variance (ANOVA) of multiple experimental factors (i.e. genotype, adaxial versus abaxial, pruning, N level, stage), and their interaction effects on each parameter was performed in the 2022 experiment. A multiple comparison of means was then performed using the LSD (least significant difference) test.

## Results

### Effect of adaxial versus abaxial measurements on leaf source activity

In the 2019 experiment, the two Y-variant genotypes exhibited an opposite trend in leaf photosynthetic rate (A): relative to their control (C) genotypes, a decrease in A and estimated parameters ($J_{max}$, $V_{cmax}$, and $T_p$) was obtained in WYJ-Y whereas an increase in these parameters was observed in ZF-Y (Fig. 1). In addition, the parameter $\alpha_s$ was also altered by the

Y modification: it became lower ($P<0.05$), particularly in the WYJ background.

The difference in photosynthetic rates between the two sides of the same leaf depended on genotypes. For light response curves (Fig. 1A–D), a great reduction (ca. 8–10%) in A at a light intensity of 2000 µmol m⁻² s⁻¹ ($A_{2000}$) was observed on the abaxial side in C genotypes, resulting in a lower estimated $J_{max}$ on the abaxial surface compared with that on the adaxial surface. In contrast, the light response curves were similar on both sides of leaves in Y-variant genotypes (difference of $A_{2000}$ less than 2%). Similar patterns were observed for CO₂ response curves (Fig. 1E–H), with greater differences between adaxial and abaxial values for parameters $V_{cmax}$, $T_p$, and $\alpha_s$ in C genotypes than in Y-variant genotypes.

### Overview of A-C_i curves from the 2020 experiment

Given the above differences between adaxial and abaxial measurements in the 2019 experiment, we continued in the 2022 experiment to use adaxial versus abaxial measurements for all nitrogen×pruning combinations as a means to manipulate within-leaf sink-source ratios. In addition, measurements were made for three different stages and at two O₂ levels. All the $A-C_i$ curves obtained are shown in Supplementary Fig. S2.

Differences in measured $A-C_i$ curves and in the estimated $A_j$-to-$A_p$ transition point between 21% and 2% O₂ (Supplementary Fig. S2) agreed with those theoretically expected for TPU limitation either with (Supplementary Fig. S2A) or without (Supplementary Fig. S2B) glycolate carbon exit from the photorespiratory pathway (Harley and Sharkey, 1991; Busch et al., 2018). Thus, combined data from the two O₂ levels were fit to estimate TPU parameters.

### Estimated triose phosphate utilization capacity

Values of $T_p$ estimated for pruned and non-pruned plants under two nitrogen levels (N1 and N2) and at three growth stages in the 2022 experiment are shown in Fig. 2. As expected, the rate of TPU ($T_p$; Fig. 2A–D; Supplementary Table S1) and photosynthetic rate at a light intensity of 1500 µmol m⁻² s⁻¹ ($A_{1500}$; Supplementary Table S2) increased with the addition of N fertilizer and decreased with advancing growth stage. Significant effects mainly occurred from flowering onwards. At 15 DAF, there were no significant effects of panicle pruning on $T_p$ except for an increase of $T_p$ in WYJ-C at N1 (Fig. 2A). In line with the results of the 2019 experiment (Fig. 1), values of $T_p$ from the adaxial measurements were generally higher than those from the abaxial measurements (Fig. 2E).

### Estimated proportion of photorespiratory carbon exited as serine

The effects of N treatments on the values of $\alpha_s$ are shown in Fig. 3. The high N level generally increased $\alpha_s$ ($P<0.05$). But
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Effects of adaxial versus abaxial measurement on the photosynthetic parameters in four rice genotypes (data measured at the tillering stage in the 2019 experiment). (A–D) Light-response curves of photosynthesis (A) at the CO₂ concentration of 400 µmol mol⁻¹. (E–H) CO₂-response curves at the light intensity of 1000 µmol m⁻² s⁻¹, for rice control (C) genotypes (circles) and their yellower-leaf (Y) variant genotypes (triangles). Data shown as the mean of four replicates (±SE) for each genotype, with black symbols representing measurement on adaxial surface of leaves and red symbols representing measurement on abaxial surface of leaves. WYJ and ZF are the abbreviations of two genetic backgrounds Wuyunjing 3 and Zhefu 802, respectively. For (A–D), the curves are drawn from Equation 6 using fitted parameter values. The estimated maximum linear electron transport under saturating light (*J*<sub>max</sub>, µmol m⁻² s⁻¹), net photosynthesis rate under light intensity of 2000 µmol m⁻² s⁻¹ (*A*<sub>2000</sub>, µmol m⁻² s⁻¹), and the percentage difference in the *J*<sub>max</sub> and *A*<sub>2000</sub> (calculated as [(Adaxial–Abaxial)/Adaxial]×100) are listed. For (E–H), the curves representing *A*<sub>c</sub>- (dashed curve), *A*<sub>r</sub>- (dotted curve), and *A*<sub>p</sub>-limited (full curve) parts are drawn from Equation 6 using fitted values of the parameters: the estimated maximum rate of Rubisco carboxylation (*V*<sub>cmax</sub>, µmol m⁻² s⁻¹), rate of triose phosphate utilization (*T*<sub>p</sub>, µmol m⁻² s⁻¹), and the proportion of glycolate carbon exported from the photorespiratory pathway in the form of serine (*α*<sub>S</sub>). The different letters indicate statistical significance at the *P*<0.05 level for the estimated parameters between adaxial and abaxial measurements, and the asterisks represent significant differences (*P*<0.05) between C genotype and its Y variant.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Effects of altered sink-source ratios on parameter *T*<sub>p</sub> (the 2022 experiment). (A–D) The rate of triose phosphate utilization (*T*<sub>p</sub>, based on measurements on the adaxial leaf surface) for rice control (C) genotypes and their yellower-leaf (Y) variant genotypes of intact or panicle-pruned plants at tillering (TS), flowering (FS), and 15 days after flowering (DAF) stages under low-nitrogen (N1, white bars) and high-nitrogen (N2, dashed bars) levels. The value of each bar representing the mean ±SE of four replicates was estimated by fitting curves to CO<sub>2</sub> exchange data (see Supplementary Fig. S2). For intact plants (no pruning), different letters indicate statistical significance at the *P*<0.05 level between three stages within each genotype–nitrogen combination, and the asterisks in black represent significant differences (*P*<0.05) between N1 and N2 levels within each genotype and stage. The asterisk in red represents significant difference (*P*<0.05) for a given genotype–nitrogen combination between pruned and un-pruned plants at 15 DAF stage. WYJ and ZF are the abbreviations of two genetic backgrounds: cv. Wuyunjing 3 and cv. Zhefu 802. (E) Comparisons of the values of *T*<sub>p</sub> measured at adaxial surface versus those measured at abaxial surface. The percentage is the average of adaxial relative to abaxial parameters and the diagonal line is the 1:1 line. Data represented by different colours and symbols are from C genotypes (circles) and Y-variant genotypes (triangles) of intact (open symbols) and panicle-pruned (filled symbols) plants at TS (blue), FS (red), and 15 DAF (green) stage. Each point represents the mean of three or four replicates.</figcaption></figure>

growth-stage effects were more complex, as αs varied more among
growth stages in C genotypes than in Y variants (Fig. 3A–D). The
estimated αs of WYJ-C declined significantly (*P*<0.05) at 15 DAF
after an increase at FS, whereas that of ZF-C decreased along all
growth stages. The estimated αs was negatively correlated with
SLA, an indicator of leaf thinness (Supplementary Fig. S3).

Unlike T_p, α_s was greatly decreased by pruning in all geno-
types, especially at N2. There was a significant interaction between
pruning and N level on α_s (P<0.001; Supplementary Table S3).
In addition, compared with T_p, α_s differed more between adaxial
and abaxial measurements (by ~24%), with larger differences in
C genotypes than in Y-variant genotypes, especially at TS and FS
stages (Fig. 3E). No interactions of measurement side with N level
or growth stage were found for α_s (Supplementary Table S3).

Correlations between triose phosphate utilization
parameters and leaf nitrogen content

In general, T<sub>p</sub> and α<sub>s</sub> were positively correlated with SLN
across all N levels and growth stages, but correlations were
genotype-dependent (Fig. 4). Given the smaller α<sub>s</sub> values in
Y genotypes (Figs 1, 3), slopes were smaller and correlations
were poorer, particularly when measuring the abaxial leaf side,
compared with the C genotypes.

The threshold Cᵢ values (at which TPU became lim-
iting) estimated by the two methods were highly consistent
(Supplementary Fig. S4). The threshold Cᵢ increased with
advancing growth stage. It was negatively correlated with
Tₚ (R²=0.51, P<0.001; Fig. 5A) and with SLN (R²=0.42,
P<0.001; Fig. 5B).

Triose phosphate utilization-limited photosynthesis in relation to whole-plant sink limitation

The variation in A₁₅₀₀ of each genotype–nitrogen combina-
tion, either across stages or across pruning levels, was positively
correlated with Tₚ for adaxial (Supplementary Fig. S5A) or
abaxial surfaces (Supplementary Fig. S5B) of leaves. A posi-
tive linear correlation of Tₚ with single-culm sink–source ratio
(R²=0.69; Fig. 6) or whole-plant sink–source ratio (R²=0.60;
Supplementary Fig. S6) was observed at the grain-filling stage.
Note that data points presented on the line x=0 in these fig-
ures for panicle-pruned plants (thus, panicle sink was zero)
were in a similar range of Tₚ (ca. 2–9 µmol m⁻² s⁻¹) of those
plants without pruning. This suggests that, overall, there was
little observable effect of panicle pruning on Tₚ. We then com-
pared the correlation between Tₚ and SLN for non-pruned
and panicle-pruned plants separately (Fig. 7A–D). The pruned
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Effects of various growth stages and nitrogen levels on parameter αs, the proportion of glycolate carbon exported from the photorespiratory pathway in the form of serine (the 2022 experiment). (A–D) αs (based on measurements on the adaxial leaf surface) for rice control (C) genotypes and their yellower-leaf (Y) variant genotypes of intact or panicle-pruned plants at tillering (TS), flowering (FS), and 15 days after flowering (DAF) stages under low-nitrogen (N1, white bars) and high-nitrogen (N2, dashed bars) levels. The value of each bar representing the mean ±SE of four replicates was estimated by fitting curves to CO₂ exchange data (see Supplementary Fig. S2). For intact plants (no pruning), different letters indicate statistical significance at the *P*<0.05 level between three stages within each genotype–nitrogen combination, and the asterisks in red represent significant differences (*P*<0.05) for a given genotype–nitrogen combination between pruned and un-pruned plants at 15 DAF stage. WYJ and ZF are the abbreviations of two genetic backgrounds: cv. Wuyunjing 3 and cv. Zhefu 802. (E) Comparison of the values of αs measured at adaxial surface versus those measured at abaxial surface. The percentage is the average of adaxial relative to abaxial parameters and the diagonal line is the 1:1 line. Data represented by different colours and symbols are from C genotypes (circles) and Y-variant genotypes (triangles) of intact (open symbols) and panicle-pruned (filled symbols) plants at TS (blue), FS (red), and 15 DAF (green) stage. Each point represents the mean of three or four replicates.</figcaption></figure>

plants still had close Tₚ correlations with SLN, but the relation-
ship deviated from (e.g. the slope became smaller in most cases)
that of the non-pruned plants.

Sink limitation caused by panicle pruning also exerted sig-
nificant impacts on whole-plant traits, such as increased leaf
area, leaf and stem dry weight, and total leaf nitrogen (Table 1).
As a result of increased N accumulation in the flag leaf (Fig.
7E–H), panicle pruning lowered leaf photosynthetic nitrogen-
use efficiency (PNUE) for each combination of genotype and
N level (Fig. 7I–M; *P*<0.001, Supplementary Table S3).

Discussion

In response to sink–source (im)balance, plants can adjust physiologial processes at different scales, and these scales can be inter-connected involving dynamic feedback. We hypothesized that the sub-foliar sink–source (im)balance involving triose phosphate utilization (TPU) is regulated by whole-plant sink–source relationships. We used yellower-leaf (Y) modification and adaxial versus abaxial illumination, and 21% versus 2% O₂ gas mixture to alter the leaf-scale source activity. Panicle pruning was used to alter whole-plant sink/source ratios. The factorial design involving these two scales enabled linking them. By observing plants under different N supply conditions

and at three growth stages, we introduced additional variation
of source and sink capacity and enabled the establishment of
parameter relationships, notably including TPU-limited pho-
tosynthesis and photorespiration-associated N assimilation.

Adaxial versus abaxial measurements on leaf-colour genotypes as a means to alter leaf source activity

In the 2019 experiment using rice genotypes of contrast-
ing leaf colour, a TPU limitation on leaf photosynthesis was
observed (Fig. 1). The yellower-leaf variants (Y) differed from
their control (C) genotypes in photosynthetic capacity (i.e.
A<sub>2000</sub>) and underlying parameters (J<sub>max</sub>, V<sub>cmax</sub>, and T<sub>p</sub>), and the
difference was mostly expressed when comparing response
curves measured under adaxial versus abaxial illumination (Fig.
1). Zhou *et al.* (2023) demonstrated differences in leaf photo-
synthetic capacity between C and Y genotypes were associated
with intra-leaf photosynthetic N reallocation of the surplus
N resources liberated by decreased investment in chlorophyll.
However, modifying leaf colour changed not only intra-leaf N
partitioning but also leaf morphology, such as increased SLA
(i.e. thinner leaves) in Y-genotypes (Supplementary Table S2).

This genotypic difference in leaf thickness probably con-
tributed to our result that when light illuminated the abaxial
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Relationship between photosynthetic parameters and leaf nitrogen content (based on measurements on the un-pruned plants in the 2022 experiment). (A–H) Relationship between triose phosphate utilization rate ($T_p$) and specific nitrogen content (SLN). (I–P) Relationship between the proportion of glycolate carbon exported from photorespiratory pathway in the form of serine ($\alpha_5$) and SLN. Data represented by different colours and symbols are from tillering (TS, blue), flowering (FS, red), and 15 days after flowering (DAF) (green) stage under low-nitrogen (N1, open symbols) and high-nitrogen (N2, filled symbols) levels, with circles for rice control (C) genotypes and triangles for their yellower-leaf (Y) variant genotypes. Linear regressions were fitted for each genotype with four or five replicates across two nitrogen levels and three stages. The significance of each correlation is shown by asterisks: \*P<0.05, \*\*P<0.01, \*\*\*P<0.001. WYJ and ZF are the abbreviations of two genetic backgrounds: cv. Wuyunjing 3 and cv. Zhefu 802.</figcaption></figure>

surface, the decrease in leaf photosynthetic capacity parameters was small in Y genotypes but greater in C genotypes (Fig. 1). This suggests that our C genotypes, like plants in previous reports (e.g. Soares *et al.*, 2008), had an adaptive advantage to the adaxial illumination, which is the predominant condition occurring in the field. As the carbon sinks of the leaf and the whole plant were unchanged, altered leaf photosynthetic capacity via adaxial versus abaxial illumination during gas exchange measurement will alter the sink-source ratio, and this was particularly the case in the C genotypes.

## Effects of altered sink-source ratios on leaf triose phosphate utilization limitation

The occurrence of the TPU limitation requires a high photosynthetic rate (Yang *et al.*, 2016). We chose a saturating light intensity (1500 µmol m⁻² s⁻¹) and CO₂ levels up to 1800 µmol mol⁻¹ for measuring A–Cᵢ curves in the 2022 experiment. In our study, A₁₅₀₀ was highly correlated with Tₚ in all cases (Supplementary Fig. S5), reflecting a parallel change

between them. Y modification reduced leaf Tₚ and A₁₅₀₀ in WYJ background but increased them in ZF background (Fig. 2A–D; Supplementary Tables S1, S2); adaxial illumination gave higher Tₚ than abaxial, especially for C genotypes (Fig. 2E; Supplementary Table S1). Strong correlations between Tₚ and SLN in each genotype (Fig. 4) suggested that N always exerted a positive effect on Tₚ. We also found that the higher the leaf N content (resulting in higher A₁₅₀₀), the lower was the threshold Cᵢ where leaf photosynthesis became TPU-limited (Supplementary Fig. S2), as shown by the negative relationship between the threshold Cᵢ and Tₚ or SLN (*P*<0.001; Fig. 5). This relationship was built from data across three growth stages, reflecting dynamic changes in the TPU limitation during rice development.

During the vegetative phase, there is no sink demand from panicles or grains, and all assimilates are used for vegetative organ growth (i.e. absence of a dominant sink like the panicle). In this phase, growing leaves rich in N resources serve as both source and sink organs. The high photosynthetic potential of young plants feeds a plastic (partly facultative) demand exerted
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** The threshold $C_i$ values in relation to leaf physiological parameters. (A) Relationship between threshold $C_i$ and the rate of triose phosphate utilization ($T_p$) (data based on measurements on the adaxial surface of leaves in the 2022 experiment). (B) Relationship between threshold $C_i$ and specific leaf nitrogen (SLN). The threshold $C_i$ represents the transition point where the photosynthesis-limiting process changed from electron transport to TPU, derived from CO₂ response curves (see Supplementary Fig. S2). Data represented by different colours and symbols are from tillering (TS, blue), flowering (FS, red), and 15 days after flowering (DAF) (green) stage under low-nitrogen (N1, open symbols) and high-nitrogen (N2, filled symbols) levels, with circles for rice control (C) genotypes and triangles for their yellower-leaf (Y) variant genotypes. Linear regressions were fitted for overall data with the significance of each correlation shown by asterisks: ***$P<0.001$.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Relationship between the rate of triose phosphate utilization ($T_p$, based on measurements on the adaxial leaf surface) and single-culm sink-source ratio. Here, following Fabre et al. (2020), the ratio of flag leaf area (source) to the fertile spikelet number of the panicle (sink) on the culm was used as an indicator of the single-culm sink-source ratio (also see the text). Data are for rice control (C) genotypes (circles) and yellower-leaf (Y) variant genotypes (triangles) from grain-filling stage under low-nitrogen (N1, open symbols) and high-nitrogen (N2, filled symbols) levels in the 2022 experiment, with cv. Wuyunjing 3 (WYJ) in black and cv. Zhefu 802 (ZF) in red. For those plants with panicle pruning, we define their sink-source ratio to be zero, so all their data points fall on the y-axis. Linear regression was fitted for data (representing no pruning) with the significance of the correlation shown by asterisks: ***$P<0.001$.</figcaption></figure>

by organ development, probably explaining why TPU limitation occurred at a lower $C_i$ level (ca. 400 µmol mol⁻¹; Fig. 5).

After flowering, carbon assimilates are exclusively used to support grain growth, and N resources are mobilized from vegetative organs, particularly from leaves (Sinclair and de Wit, 1975). The resulting decrease in $A_{1500}$ and $T_p$ (Fig. 2; Supplementary Tables S1, S2), coupled with increased sink demand, alleviated the extent of TPU limitation.

Our study provides insights on how altered sink-source relationships influence $T_p$ and thus leaf source activity. The estimated $T_p$ and the $C_i$ threshold for the onset of TPU limitation were associated with leaf N content across growth stages. Thus, leaf N content not only determines leaf photosynthetic capacity (Nakano et al., 1995), but also modulates TPU limitation in response to sink-source imbalance.

### Nitrogen assimilation increases photosynthesis under potential triose phosphate utilization limitation

Assimilating $NO_3^-$ via exporting glycolate carbon from the photorespiratory pathway in the form of amino acids can contribute to photosynthetic carbon uptake (Busch et al., 2018; Busch, 2020; Fu et al., 2022). Our $A-C_i$ curves, indicating $A$ was higher under 21% $O_2$ than under 2% $O_2$ conditions, confirmed this—although this advantage from photorespiration diminished with advancing growth stages (Supplementary Fig. S2). The increase in $A$ largely depends on the extent of glycolate carbon exported from the photorespiratory pathway (Bauwe et al., 2010; Busch et al. 2018; Yin et al. 2021; also see Supplementary Fig. S2A, B). Fu et al. (2022) demonstrated that the carbon flow out of the pathway was primarily in the form of serine, with a proportion of 23–41% in tobacco plants. Our estimates of parameter $\alpha_S$ from model analysis (up to 0.37;
<figure><img src="image_8.png" /><figcaption>**Fig. 7.** The effects of panicle pruning on leaf photosynthetic parameters (based on measurements on the adaxial leaf surface in the 2022 experiment). (A–D) Relationship between the rate of triose phosphate utilization ($T_p$) and specific leaf nitrogen (SLN) for the intact and panicle-pruned plants. (E–H) Effect of panicle pruning on SLN. (I–M) Leaf photosynthetic nitrogen-use efficiency (PNUE) at tillering (TS), flowering (FS), and 15 days after flowering (DAF) stage (see Supplementary Table S2 for the definition of PNUE). Linear regressions in (A–D) were fitted for each genotype with four or five replicates under two nitrogen levels; the significance of each correlation is shown by asterisks: \*P<0.05, \*\*P<0.01, \*\*\*P<0.001. Data in (E–M) represent the mean ±SE of four replicates; the asterisks represent significant differences (*P*<0.05) within each genotype-nitrogen combination between unpruned and pruned plants. The data in (A–H) are from the 15 DAF stage, and the data in (A–D) and (I–M) represent the values for rice control (C) genotypes (circles) and their yellower-leaf (Y) variant genotypes (triangles) of the intact (black) and panicle-pruned (red) plants under low-nitrogen (N1, open symbols) and high-nitrogen (N2, filled symbols) levels. WYJ and ZF are the abbreviations of two genetic backgrounds: cv. Wuyunjing 3 and cv. Zhefu 802.</figcaption></figure>

Supplementary Table S1) are in line with the measured values of Fu *et al.* (2022) as well as with the modelling results of Busch *et al.* (2018). Based on modelled $\alpha_S$, we further assessed the effect of various experimental factors on photorespiration-associated N assimilation. We found that similar to the effect on $T_p$, leaf N content is also critical to $\alpha_S$, as evidenced by correlations between $\alpha_S$ and SLN (Fig. 4I–P). However, this positive effect of N level disappeared under Y modification (Supplementary

Table S3), since both Y genotypes had lower $\alpha_S$ than their corresponding C genotypes (Figs 1, 3; Supplementary Table S1). In conjunction with this, there was a negative relationship between $\alpha_S$ and SLA (Supplementary Fig. S3), suggesting that the thinner leaves associated with Y modification may have limited $NO_3^-$ assimilation. This limiting effect was enhanced by illuminating the abaxial surface of leaves during measurements (Fig. 3E; Supplementary Table S3).
In our experiment, urea was used as the N source in fertilizing plants. Busch et al. (2018) found that α₅ was higher in sunflower plants fertilized with NO₃⁻-N than those with NH₄⁺-N. Whether this is also the case when applied to our rice plants of different leaf colours remains to be investigated. Previous studies (Reich et al., 2006; Bloom et al., 2012, 2014) reported that elevated growth CO₂ (resulting in increased source) inhibited the NO₃⁻ assimilation in C₃ plants. Here, we found a similar result with a significant drop in α₅ for the plants after panicle pruning (resulting in decreased sink) (Fig. 3). However, the underlying mechanisms might differ. Elevated growth CO₂ not only decreased photorespiration but also diluted leaf N concentration via increased biomass (Yin, 2013; Igarashi et al., 2021). In contrast, panicle pruning resulted in an increased N content in leaves (Table 1; Fig. 7E–H). Nevertheless, despite higher leaf N, a lower α₅ (thus, probably less N assimilation) was observed in leaves of panicle-pruned plants, suggesting a dominating role of whole-plant sink–source ratio in the control of leaf N metabolism.

## Feedback inhibition of whole-plant sink limitation on leaf photosynthesis

It has long been observed that the plant sink–source ratio can affect leaf photosynthesis (Tanaka and Fujita, 1974; Crafts-Brandner and Egli, 1987; Arp, 1991; Li et al., 2015; Aslani et al., 2020). In line with this, studies indicated that a larger sink capacity can increase the effect of elevated atmospheric CO₂ on photosynthesis and yield (Erice et al., 2011; Hasegawa et al., 2013; Rossi et al., 2015; Kikuchi et al., 2017; Dingkuhn et al., 2020). Our finding of a positive correlation between Tₚ and the sink–source ratio for single culm (Fig. 6) or for the whole plant (Supplementary Fig. S6) confirmed the role of sink size in modulating source activity of leaves. The reduced Tₚ under sink limitation is probably associated with the accumulation of sucrose in leaf photosynthetic tissues (Fabre et al., 2019, 2020). This may give a signal regulating the activity of sucrose-phosphate synthase (SPS) through transduction of SnRK1 protein kinases (Halford and Hey, 2009), and the SPS feedback inhibition on sucrose synthesis can decrease export of triose phosphates from the chloroplast (McClain and Sharkey, 2019). Our results also showed that panicle pruning dramatically increased the dry matter accumulation in leaves and stems (Table 1).

Given these considerations, one would expect that for the 15 DAF stage, panicle-pruned plants would have lower Tₚ compared with intact plants where the TPU limitation was alleviated. Surprisingly, distinct from previous findings (Fabre et al., 2019; Nomura et al., 2022), Tₚ was little affected under this apparently sink-limited state (Figs 2, 6). In the study of Fabre et al. (2019), elevated CO₂ (increased source) coupled with pruning treatment (decreased sink) imposed a dual effect on Tₚ, which might contribute to more significant sink limitation and feedback effect in their study than in our study. However, the mechanism behind this difference could also be

attributed to a larger amount of N accumulated in the leaves of our panicle-pruned plants (Fig. 7E–H). After removal of all panicles, no N was required to be remobilized from leaves to support grain growth and more N remained in leaves. Leaf N is the most important resource for the photosynthetic machinery (Nakano et al., 1995), as shown by our close relationship between Tₚ and SLN, even in pruned plants (Fig. 7A–D). Thus, we posit that the feedback inhibition of sink limitation on Tₚ still occurred in our panicle-pruned plants, but the inhibitory effects from sink limitation were offset by the increased SLN as a consequence of the smaller N remobilization from leaves, thereby resulting in no observable impact of pruning on Tₚ in leaves. The increased SLN did not result in higher N assimilation as α₅ was lower in panicle-pruned plants (Fig. 3; Supplementary Table S1); as a result, these plants might not benefit from amino acid export for higher CO₂ uptake rates, but instead, had similar or lower A₁₅₀₀ compared with the control plants (Supplementary Table S2). Because of the increased SLN (Fig. 7E–H) accompanied with no or little increase in A₁₅₀₀, leaf PNUE decreased in the panicle-pruned plants (Fig. 7I–M). Taking these together, our results suggest that Tₚ and leaf photosynthesis during grain filling are controlled by the whole-plant sink demand and N budgets.

## Concluding remarks

We have shown that the differences in photosynthetic rates between adaxially and abaxially illuminated leaves and the extent of such differences depends on leaf colours of the genotypes. Based on this finding, we further assessed how TPU limitation of photosynthesis, involving photorespiration-associated N assimilation, can be affected by altered sink–source ratios at different scales. We found:

(i) Higher leaf N (observed at early growth stages or high N inputs) caused TPU limitation at relatively low intercellular CO₂ concentration.

(ii) The proportion of photorespiratory glycolate-carbon exported as serine (α₅) was positively correlated with leaf N content, suggesting that photorespiration was involved in leaf N assimilation more in high-N than low-N leaves. However, values of α₅ were smaller in yellower-leaf genotypes, under illumination of abaxial leaf sides, and when panicles were removed.

(iii) Absence of observable effect of panicle pruning on the rate of TPU (Tₚ) suggested that the feedback inhibition of photosynthesis by whole-plant sink limitation was offset by the positive effect of increased leaf N content, caused by lesser N remobilization.

Thus, our results revealed the crucial role of leaf N in affecting photosynthetic parameters Tₚ and α₅, and thus TPU limitation of photosynthesis. Likewise, N resources modulate the link between leaf-level TPU limitation and whole-plant sink limitation during rice grain filling. The latter link could be
<table><caption>**Table 1.** Values (means ±SE among four replicates) of rice sink and source organs for single culm and whole plant in the control (C) genotypes and the yellower-leaf variant (Y) genotypes at 15 d after flowering stage in the 2022 experiment.</caption><thead><tr><th rowspan="2">N level</th><th rowspan="2">Background</th><th rowspan="2">Genotype</th><th colspan="2">Single culm</th><th colspan="2">Whole plant</th><th rowspan="2">Leaf dry weight (g)</th><th rowspan="2">Stem dry weight (g)</th><th rowspan="2">Total leaf nitrogen (g N pot<sup>-1</sup>)</th></tr><tr><th>Spikelets per culm (no.)</th><th>Flag leaf area (cm<sup>2</sup>)</th><th>Total spikelet (no.)</th><th>Leaf area (cm<sup>2</sup> pot<sup>-1</sup>)</th></tr></thead><tbody><tr><td rowspan="6">No pruning</td><td rowspan="3">N1</td><td>WYJ</td><td>C</td><td>47 ± 5</td><td>15.5 ± 0.7</td><td>543 ± 49</td><td>1259.5 ± 27.8</td><td>4.8 ± 0.1</td><td>17.7 ± 0.8</td><td>0.098 ± 0.003</td></tr><tr><td>WYJ</td><td>Y</td><td>30 ± 3</td><td>18.1 ± 0.7</td><td>336 ± 24</td><td>1361.5 ± 73.0</td><td>4.3 ± 0.3</td><td>8.2 ± 0.5</td><td>0.130 ± 0.007</td></tr><tr><td>ZF</td><td>C</td><td>101 ± 9</td><td>28.7 ± 4.6</td><td>811 ± 21</td><td>1400.8 ± 59.0</td><td>7.7 ± 0.5</td><td>34.6 ± 0.9</td><td>0.082 ± 0.006</td></tr><tr><td rowspan="3">N2</td><td>WYJ</td><td>C</td><td>114 ± 7</td><td>29.7 ± 4.1</td><td>893 ± 55</td><td>2158.3 ± 97.5</td><td>9.4 ± 0.6</td><td>19.4 ± 1.2</td><td>0.168 ± 0.008</td></tr><tr><td>WYJ</td><td>Y</td><td>70 ± 14</td><td>17.8 ± 2.8</td><td>827 ± 55</td><td>1597.7 ± 30.7</td><td>6.2 ± 0.1</td><td>23.5 ± 1.5</td><td>0.129 ± 0.004</td></tr><tr><td>ZF</td><td>C</td><td>46 ± 6</td><td>19.7 ± 1.4</td><td>797 ± 86</td><td>1665.5 ± 86.3</td><td>5.9 ± 0.3</td><td>12.2 ± 1.0</td><td>0.186 ± 0.006</td></tr><tr><td rowspan="6">Pruning</td><td rowspan="3">N1</td><td>WYJ</td><td>C</td><td>105 ± 5</td><td>26.7 ± 2.7</td><td>1324 ± 23</td><td>1898.0 ± 50.1</td><td>9.0 ± 0.6</td><td>34.9 ± 1.1</td><td>0.100 ± 0.009</td></tr><tr><td>WYJ</td><td>Y</td><td>132 ± 12</td><td>31.2 ± 4.1</td><td>1421 ± 54</td><td>2702.5 ± 50.8</td><td>11.6 ± 0.6</td><td>21.8 ± 1.9</td><td>0.232 ± 0.020</td></tr><tr><td>ZF</td><td>Y</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td rowspan="3">N2</td><td>WYJ</td><td>C</td><td>—</td><td>13.3 ± 2.1</td><td>—</td><td>1304.6 ± 43.4</td><td>5.6 ± 0.1</td><td>24.8 ± 1.0</td><td>0.106 ± 0.005</td></tr><tr><td>WYJ</td><td>Y</td><td>—</td><td>15.7 ± 1.2</td><td>—</td><td>1569.5 ± 33.7</td><td>5.8 ± 0.2</td><td>15.5 ± 1.1</td><td>0.179 ± 0.005</td></tr><tr><td>ZF</td><td>C</td><td>—</td><td>29.1 ± 2.4</td><td>—</td><td>1684.0 ± 76.4</td><td>10.5 ± 0.6</td><td>43.9 ± 2.6</td><td>0.101 ± 0.007</td></tr><tr><td rowspan="6">Analysis of variance</td><td rowspan="3">Genotype</td><td>N level</td><td>C</td><td>6.9*</td><td>3.6</td><td>135.5***</td><td>167.8***</td><td>114.4***</td><td>43.9***</td><td>171.4***</td></tr><tr><td>Panicle pruning</td><td>C</td><td>—</td><td>0.1</td><td>—</td><td>34.4***</td><td>105.8***</td><td>177.0***</td><td>72.8***</td></tr><tr><td>GenotypexN level</td><td>0.5</td><td>0.7</td><td>2.1</td><td>8.9***</td><td>5.2**</td><td>3.6*</td><td>17.7***</td><td>8.5***</td></tr><tr><td>Genotypexpanicle pruning</td><td>—</td><td>0.7</td><td>—</td><td>3.58*</td><td>14.5***</td><td>—</td><td>—</td><td>—</td></tr><tr><td>N levelxpanicle pruning</td><td>—</td><td>1.6</td><td>—</td><td>0.6</td><td>6.2*</td><td>2.8</td><td>—</td><td>—</td></tr><tr><td>GenotypexN levelxpanicle pruning</td><td>—</td><td>0.2</td><td>—</td><td>1.8</td><td>4.4**</td><td>10.4***</td><td>—</td><td>0.6</td></tr></tbody></table>

In the analysis of variance, the significance is shown by asterisks: *P<0.05, **P<0.01, ***P<0.001, according to the LSD test. Note, we use 'genotype' rather than 'Y modification' as a fixed factor mainly because Y modification produces different effects on leaf photosynthetic physiology between WYJ (cv. Wuyunjing 3) and ZF (cv. Zhefu 802) backgrounds (see Results).
masked by the whole-plant N budget, providing a contribut-
ing factor (additional to what has been stated in Introduction)
about why the sink feedback on, and TPU limitation to, leaf
photosynthesis cannot always be observed experimentally.
This also adds the complication of disentangling the interac-
tion and causality between observed A₁₅₀₀ and Tₚ in relation
to leaf N content. Our results have important implications
for modelling crop production in response to a future high-
CO₂ environment, where a delicate balance between source
and sink in plants becomes increasingly altered, leaf photo-
synthesis is expected to be increasingly limited by TPU, and
plant N resources tend to be diluted by the greater biomass
(Dingkuhn et al., 2020). This warrants more research for better
understanding of the N regulatory mechanism in this con-
text so as to effectively screen adaptive traits in rice genotypes
for improved crop productivity and nutritional value under
futural climatic conditions.

Supplementary data

The following supplementary data are available at JXB online.

Fig. S1. The photorespiratory pathway (involving chloro-
plast, peroxisome, and mitochondrion), and its connection
with the Calvin–Benson–Bassham (CBB) cycle and nitrogen
(N) assimilation (revised from Busch, 2020).

Fig. S2. CO₂-response curves for rice control (C) genotypes and their yellower-leaf (Y) variant genotypes, on both sides of the leaves at three stages, under 21% O₂ and 2% O₂ conditions.

Fig. S3. Relationship between the proportion of glycolate carbon exported from photorespiratory pathway in the form of serine (α₅, based on measurements on the adaxial leaf surface) and specific leaf area (SLA).

Fig. S4. Comparisons of the threshold Cᵢ (intercellular CO₂ levels) derived from two methods under adaxial and abaxial measurements.

Fig. S5. Relationship between light-saturated leaf photosyn-
thesis rate (A<sub>1500</sub>) and the rate of triose phosphate utilization
(T<sub>p</sub>) based on adaxial and abaxial measurements.

Fig. S6. Relationship between the Tp (based on measure-
ments on the adaxial leaf surface) and whole-plant sink–source
ratio.

Table S1. Modelled photosynthetic parameters for rice control (C) genotypes and their yellower-leaf (Y) variant genotypes at three stages under low-nitrogen (N1) and high-nitrogen (N2) levels measured from both sides of the leaves in the 2022 experiment.

Table S2. Leaf photosynthetic characteristics for rice control (C) genotypes and their yellower-leaf (Y) variant genotypes at three stages under low-nitrogen (N1) and high-nitrogen (N2) levels measured from both sides of leaves in the 2022 experiment.

Table S3. Summary of analysis of variance of leaf photo-
synthetic variables in response to genotype, panicle pruning,

abaxial versus adaxial measurements, nitrogen level, three-
stages' measurements, and their interactions.

Acknowledgements

ZhZ thanks the China Scholar Council (CSC) for funding his PhD fellowship. We thank Prof S Jianchang Yang and Junfei Gu (Yangzhou University), Dr Changquan Zhang (Yangzhou University), and Prof. Fangmin Cheng (Zhejiang University) for providing seeds used in this study.

Author contributions

XY and ZhZ: conceptualization; ZhZ and ZiZ performed the experi-
ments with the assistance of PELvdP; ZhZ analysed the data and wrote
the initial draft with input from XY and PCS; DF and MD contrib-
uted to the discussion on the sink-source relationship and improved the
manuscript structure. All authors commented on the manuscript and
approved the final version.

Conflict of interest

The authors declare no conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data availability

All data supporting the findings of this study are available within the
paper and within its supplementary data published online.

References

**Abadie C**, **Boex-Fontvieille ERA**, **Carroll AJ**, **Tcherkez G.** 2016.
*In vivo* stoichiometry of photorespiratory metabolism. *Nature Plants* **2**,
15220.

Arp WJ. 1991. Effects of source-sink relations on photosynthetic acclima-
tion to elevated CO₂. Plant, Cell & Environment **14**, 869-875.

Aslani L, Gholami M, Mobli M, Sabzalian MR. 2020. The influence of altered sink-source balance on the plant growth and yield of greenhouse tomato. Physiology and Molecular Biology of Plants **26**, 2109–2123.

Bauwe H, Hagemann M, Fernie AR. 2010. Photorespiration: players, partners and origin. *Trends in Plant Science* **15**, 330–336.

Bloom AJ, Asensio JSR, Randall L, Rachmilevitch S, Cousins AB, Carlisle EA. 2012. CO₂ enrichment inhibits shoot nitrate assimilation in C₃ but not C₄ plants and slows growth under nitrate in C₃ plants. Ecology **93**, 355–367.

Bloom AJ, Burger M, Kimball BA, Pinter PJ. 2014. Nitrate assimilation is inhibited by elevated CO₂ in field-grown wheat. Nature Climate Change **4**, 477–480.

Burnett AC, Rogers A, Rees M, Osborne CP. 2016. Carbon source-sink limitations differ between two species with contrasting growth strategies. Plant, Cell & Environment **39**, 2460–2472.
**Busch FA.** 2020. Photorespiration in the context of Rubisco biochemistry, CO₂ diffusion and metabolism. The Plant Journal **101**, 919–939.

**Busch FA, Sage RF.** 2017. The sensitivity of photosynthesis to O₂ and CO₂ concentration identifies strong Rubisco control above the thermal optimum. *New Phytologist* **213**, 1036–1051.

**Busch FA, Sage RF, Farquhar GD.** 2018. Plants increase CO₂ uptake by assimilating nitrogen via the photosynthetic pathway. Nature Plants **4**, 46–54.

**Cousins AB, Ghannoum O, Von Caemmerer S, Badger MR.** 2010. Simultaneous determination of Rubisco carboxylase and oxygenase kinetic parameters in *Triticum aestivum* and *Zea mays* using membrane inlet mass spectrometry. Plant, Cell & Environment **33**, 444–452.

**Crafts-Brandner SJ, Egli DB.** 1987. Sink removal and leaf senescence in soybean: cultivar effects. Plant Physiology **85**, 662–666.

**Dingkuhn M, Luquet D, Fabre D, Muller B, Yin X, Paul MJ.** 2020. The case for improving crop carbon sink strength or plasticity for a CO₂-rich future. Current Opinion in Plant Biology **56**, 259–272.

**Ellsworth DS, Crous KY, Lambers H, Cooke J.** 2015. Phosphorus recycling in photorespiration maintains high photosynthetic capacity in woody species. Plant, Cell & Environment **38**, 1142–1156.

**Erice G, Sanz-Sáez A, Aranjuelo I, Irigoyen JJ, Aguirreolea J, Avice JC, Sánchez-Díaz M.** 2011. Photosynthesis, N₂ fixation and taproot reserves during the cutting regrowth cycle of alfalfa under elevated CO₂ and temperature. Journal of Plant Physiology **168**, 2007–2014.

**Fabre D, Dingkuhn M, Yin X, Clément-Vidal A, Roques S, Soutiras A, Luquet D.** 2020. Genotypic variation in source and sink traits affects the response of photosynthesis and growth to elevated atmospheric CO₂. Plant, Cell & Environment **43**, 579–593.

**Fabre D, Yin X, Dingkuhn M, Clément-Vidal A, Roques S, Rouan L, Soutiras A, Luquet D.** 2019. Is triose phosphate utilization involved in the feedback inhibition of photosynthesis in rice under conditions of sink limitation? Journal of Experimental Botany **70**, 5773–5785.

**Farquhar GD, von Caemmerer S, Berry JA.** 1980. A biochemical model of photosynthetic CO₂ assimilation in leaves of C₃ species. Planta **149**, 78–90.

**Fu X, Gregory LM, Weise SE, Walker BJ.** 2022. Integrated flux and pool size analysis in plant central metabolism reveals unique roles of glycine and serine during photorespiration. Nature Plants **9**, 169–178.

**Genty B, Briantais JM, Baker NR.** 1989. The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochimica et Biophysica Acta, General Subjects **990**, 87–92.

**Halford NG, Hey SJ.** 2009. Snf1-related protein kinases (SnRKs) act within an intricate network that links metabolic and stress signalling in plants. Biochemical Journal **419**, 247–259.

**Harley PC, Loreto F, Marco GD, Sharkey TD.** 1992. Theoretical considerations when estimating the mesophyll conductance to CO₂ flux by analysis of the response of photosynthesis to CO₂. Plant Physiology **98**, 1429–1436.

**Harley PC, Sharkey TD.** 1991. An improved model of C₃ photosynthesis at high CO₂: reversed O₂ sensitivity explained by lack of glycerate reentry into the chloroplast. Photosynthesis Research **27**, 169–178.

**Hasegawa T, Sakai H, Tokida T, et al.** 2013. Rice cultivar responses to elevated CO₂ at two free-air CO₂ enrichment (FACE) sites in Japan. Functional Plant Biology **40**, 148–159.

**He A, Wang W, Jiang G, Sun H, Jiang M, Man J, Cui K, Huang J, Peng S, Nie L.** 2019. Source-sink regulation and its effects on the regeneration ability of ratoon rice. Field Crops Research **236**, 155–164.

**Igarashi M, Yi Y, Yano K.** 2021. Revisiting why plants become N deficient under elevated CO₂: importance to meet N demand regardless of the form. Frontiers in Plant Science **12**, 726186.

**Kikuchi S, Bheemanahalli R, Jagadish KSV, Kumagai E, Masuya Y, Kuroda E, Raghavan C, Dingkuhn M, Abe A, Shimono H.** 2017. Genome-wide association mapping for phenotypic plasticity in rice. Plant, Cell & Environment **40**, 1565–1575.

**Kumarathunge DP, Medlyn BE, Drake JE, Rogers A, Tjoelker MG.** 2019. No evidence for triose phosphate limitation of light-saturated leaf

photosynthesis under current atmospheric CO₂ concentration. Plant, Cell & Environment **42**, 3241–3252.

**Li G, Pan J, Cui K, Yuan M, Hu Q, Wang W, Mohapatra PK, Nie L, Huang J, Peng S.** 2017. Limitation of unloading in the developing grains is a possible cause responsible for low stem non-structural carbohydrate translocation and poor grain yield formation in rice through verification of recombinant inbred lines. Frontiers in Plant Science **8**, 1369.

**Li T, Heuvelink E, Marcelis LFM.** 2015. Quantifying the source-sink balance and carbohydrate content in three tomato cultivars. Frontiers in Plant Science **6**, 416.

**Lombardozzi DL, Smith NG, Cheng SJ, Dukes JS, Sharkey TD, Rogers A, Bonan GB.** 2018. Triose phosphate limitation in photosynthesis models reduces leaf photosynthesis and global terrestrial carbon storage. Environmental Research Letters **13**, 074025.

**Loriaux SD, Avenson TJ, Welles JM, Mcdermitt DK, Eckles RD, Riensche B, Genty B.** 2013. Closing in on maximum yield of chlorophyll fluorescence using a single multiphase flash of sub-saturating intensity. Plant, Cell & Environment **36**, 1755–1770.

**Mathan J, Singh A, Jathar V, Ranjan A.** 2021. High photosynthesis rate in two wild rice species is driven by leaf anatomy mediating high Rubisco activity and electron transport rate. Journal of Experimental Botany **72**, 7119–7135.

**McClain AM, Cruz JA, Kramer DM, Sharkey TD.** 2023. The time course of acclimation to the stress of triose phosphate use limitation. Plant, Cell & Environment **46**, 64–75.

**McClain AM, Sharkey TD.** 2019. Triose phosphate utilization and beyond: from photosynthesis to end product synthesis. Journal of Experimental Botany **70**, 1755–1766.

**Nakano H, Makino A, Mae T.** 1995. Effects of panicle removal on the photosynthetic characteristics of the flag leaf of rice plants during the ripening stage. Plant and Cell Physiology **36**, 653–659.

**Nomura K, Saito M, Ito M, Yamane S, Iwao T, Tada I, Yamazaki T, Ono S, Yasutake D, Kitano M.** 2022. Diurnal decline in the photosynthetic capacity of uppermost leaves in an eggplant canopy grown in a horticultural greenhouse. Photosynthetica **60**, 457–464.

**Reich PB, Hobbie SE, Lee T, Ellsworth DS, West JB, Tilman D, Knops JMH, Naeem S, Trost J.** 2006. Nitrogen limitation constrains sustainability of ecosystem response to CO₂. Nature **440**, 922–925.

**Rossi M, Bermudez L, Carrari F.** 2015. Crop yield: challenges from a metabolic perspective. Current Opinion in Plant Biology **25**, 79–89.

**Sharkey TD.** 1985. Photosynthesis in intact leaves of C₃ plants: physics, physiology and rate limitations. The Botanical Review **51**, 53–105.

**Sharkey TD.** 2019. Is triose phosphate utilization important for understanding photosynthesis? Journal of Experimental Botany **70**, 5521–5525.

**Sinclair TR, De Wit CT.** 1975. Photosynthate and nitrogen requirements for seed production by various crops. Science **189**, 565–567.

**Soares AS, Driscoll SP, Olmos E, Harbinson J, Arrabaça MC, Foyer CH.** 2008. Adaxial/abaxial specification in the regulation of photosynthesis and stomatal opening with respect to light orientation and growth with CO₂ enrichment in the C₄ species *Paspalum dilatatum*. New Phytologist **177**, 186–198.

**Tanaka A, Fujita K.** 1974. Nutrio-physiological studies on the tomato plant IV. Source-sink relationship and structure of the source-sink unit. Soil Science and Plant Nutrition **20**, 305–315.

**von Caemmerer S.** 2000. Biochemical models of leaf photosynthesis. Collingwood, Victoria, Australia: CSIRO publishing.

**von Caemmerer S, Farquhar GD.** 1981. Some relationships between the biochemistry of photosynthesis and the gas exchange of leaves. Planta **153**, 376–387.

**White AC, Rogers A, Rees M, Osborne CP.** 2016. How can we make plants grow faster? A source-sink perspective on growth rate. Journal of Experimental Botany **67**, 31–45.

**Yang JT, Preiser AL, Li Z, Weise SE, Sharkey TD.** 2016. Triose phosphate use limitation of photosynthesis: short-term and long-term effects. Planta **243**, 687–698.
**Yin X.** 2013. Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO₂ concentration on crop productivity. *Annals of Botany* **112**, 465–475.

**Yin X, Busch FA, Struik PC, Sharkey TD.** 2021. Evolution of a biochemical model of steady-state photosynthesis. *Plant, Cell & Environment* **44**, 2811–2837.

**Yin X, Gu J, Dingkuhn M, Struik PC.** 2022. A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement. *Journal of Experimental Botany* **73**, 3173–3188.

**Yin X, Struik PC, Romero P, Harbinson J, Evers JB, Van Der Putten PEL, Vos J.** 2009. Using combined measurements of gas exchange and

chlorophyll fluorescence to estimate parameters of a biochemical C₃ photo-synthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat (*Triticum aestivum*) canopy. *Plant, Cell & Environment* **32**, 448–464.

**Yin X, van der Putten PEL, Belay D, Struik PC.** 2020. Using photorespiratory oxygen response to analyse leaf mesophyll resistance. *Photosynthesis Research* **144**, 85–99.

**Zhou Z, Struik PC, Gu J, van der Putten PEL, Wang Z, Yin X, Yang J.** 2023. Enhancing leaf photosynthesis from altered chlorophyll content requires optimal partitioning of nitrogen. *Crop and Environment* **2**, 24–36.