# Beyond light quantity: Intermittent shading affects growth and yield more than constant shading in rice cultivars

Laura Lescroart, Frédéric Boudon, Christine Granier, Michaël Dingkuhn, Raphaël Perez

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## To cite this version:

Laura Lescroart, Frédéric Boudon, Christine Granier, Michaël Dingkuhn, Raphaël Perez. Beyond light quantity: Intermittent shading affects growth and yield more than constant shading in rice cultivars. Environmental and Experimental Botany, 2026, 241, pp.106301. 10.1016/j.envexpbot.2025.106301. hal-05461047

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Submitted on 16 Jan 2026

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Research paper

# Beyond light quantity: Intermittent shading affects growth and yield more than constant shading in rice cultivars

Laura Lescroart<sup>a,b</sup>, Frédéric Boudon<sup>a,b</sup>, Christine Granier<sup>b</sup>, Michael Dingkuhn<sup>a,b</sup>,
Raphael P.A. Perez<sup>a,b,*</sup>

## ARTICLE INFO

Keywords:

Oryza sativa L.

Shade acclimation

Shade intermittence

Grain yield

Radiation use efficiency

## ABSTRACT

Plant responses to shade are often studied under constant light reduction, but rarely under intermittent shading which is frequent in natural environments and multilayered systems such as agrivoltaics and agroforestry. We investigated in controlled environments how a 38 % reduction in daily radiation, applied either continuously or as several intermittent periods per day, affected morphology, phenology, aboveground biomass, yield components, and photosynthetic traits of 14 rice cultivars. Plants were grown under full light with 24 MJ m⁻² day⁻¹, and both shading treatments received the same daily light integral. On average, continuous shading reduced grain yield by 28 % and aboveground biomass by 27 %, whereas intermittent shading resulted in greater decreases, by 32 % and 31 %, respectively. Yield losses were mainly attributed to reduced tillering and to a lesser extent to 1000-kernel weight. Growth reduction was sub-proportional to light resources, indicating increased incident radiation use efficiency under shading. However, this increase was less pronounced under intermittent shading, chiefly attributable to the curvi-linear response of photosynthesis to light. Furthermore, a decrease in the maximum electron transport rate under intermittent shading conditions suggested reduced photosynthetic capacity as well. Despite substantial variation among cultivars in morphology and photosynthetic parameters under full light, none of these traits predicted differential responses to shading regimes. These findings highlight that diurnal light distribution strongly influences crop performance, beyond the effects of mean light reduction. Consideration of intermittent shading is thus crucial for understanding and modeling plant responses in multi-layered systems such as agrivoltaics, intercropping, and agroforestry.

## 1. Introduction

In the face of increasing land pressure and climate variability, innovative cropping systems such as agrivoltaics, intercropping, and agroforestry have emerged as promising strategies to enhance resource use efficiency and secure crop yields (Maitra et al., 2023; Widmer et al., 2024). These diversified systems aim to optimize land productivity by combining multiple plant canopies or integrating technological elements within the same spatial footprint. This approach enables more efficient use of environmental resources and contributes to greater system resilience under climatic stress (Edouard et al., 2023; Marrou et al., 2013a; Zhang et al., 2018). Keys for plant performance in these systems are the spatial and temporal partitioning of critical resources, including light, water, and nutrients, allowing plants to use available resources complementarily (Burgess et al., 2022).

Trees in agroforestry or photovoltaic panels in agrivoltaic systems modify the quantity, quality and the temporal distribution of incident solar radiation reaching the understory crops (Dufour et al., 2013; Dupraz et al., 2011). This shading can have both beneficial and detrimental effects on plant physiology and growth, depending on its intensity, duration, and the spectral composition of the transmitted light, as well as on plant species and cultivars (Barron-Gafford et al., 2019; Hussain et al., 2019b; Prakash et al., 2023; Valle et al., 2017). While moderate shading may reduce both thermal and drought stresses or improve radiation-use efficiency, excessive shading can limit photosynthesis and reduce plant production (Ramos-Fuentes et al., 2023). Understanding the interactions between light availability and plant responses in these complex systems is therefore essential to their successful design and management (A. Ghosh, 2023). Although rice is a major staple crop, irrigated rice is rarely considered in intercropping or

<footnote><sup>a</sup> CIRAD, UMR AGAP Institut, Montpellier, France</footnote>

<footnote><sup>b</sup> UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote>* Corresponding author at: CIRAD, UMR AGAP Institut, Montpellier, France.
E-mail address: raphael.perez@cirad.fr (R.P.A. Perez).</footnote>
agroforestry systems, as these systems typically requires full-field flooding. Consequently, research on agroforestry and intercropping systems has focused primarily on rainfed upland rice (Alridiwirsah et al., 2019; Perez et al., 2024). However, irrigated rice is now increasingly being explored in agrivoltaic systems, due to its widespread cultivation in large land areas (Sojib Ahmed et al., 2022). In addition, several recent studies have highlighted the potential of specific irrigated rice cultivars to perform well under agrivoltaic conditions (Gonocruz et al., 2021; Jo et al., 2022; Thum et al., 2025).

Most studies assessing the effects of shading on crop physiology and yield have traditionally employed reduction in light intensity, wherein light is uniformly reduced throughout the entire photoperiod either in controlled conditions or using a shading screens in open-field conditions (Hussain et al., 2019a; Li et al., 2010). These approaches have given insights into plant responses to shade (Valladares and Niinemets, 2008). Moderate shading usually causes changes in cereal phenology, morphology, physiology and yield. Phenological and morphological changes include earlier flowering and internode elongation, increased leaf area, thinner leaves (i.e. increased specific leaf area), and reduced tillering (Li et al., 2010). As for yield and biomass, low to moderate shading (providing less than 50 % shading) can result in no change, an enhancement, or a yield reduction that is sub-proportional to the light reduction (Kobata et al., 2000; Li et al., 2020; Song et al., 2022; Wang et al., 2015; Yang et al., 2007). However, these setups do not reflect the complex and dynamic nature of light environments encountered in natural field conditions (Matsubara, 2018). In natural vegetation and agricultural systems, plants are exposed to highly variable light intensities that fluctuate both diurnally and seasonally during the plant cycle. This variability can arise from multiple sources, including transient cloud cover, atmospheric pollutants, and, in diversified systems, from mobile or structural sources of shading such as overstory trees, crop canopies, or photovoltaic panels (Smith and Berry, 2013). The spatial configuration, size, height, spacing, and orientation of these shading elements strongly influence the spatiotemporal dynamics of light availability at the crop level (Perez et al., 2024; Sarr et al., 2024). In particular for agrivoltaic systems, these sun-shade cycles caused by changing sun angles are referred to as “clearing” or “sun gaps”, typically ranging from 60 to over 120 min, depending on the design of the photovoltaic structure (Willockx et al., 2022). Such dynamic light regimes have important implications for plant photosynthetic acclimation and resource use efficiency, as they influence plant response differently from constant light reduction (Kaiser et al., 2019; Pearcy, Sassenrath, 1996).

Plant responses are governed not only by the total daily light integral (DLI) but also by the temporal dynamics of light availability (Alter et al., 2012; Yin and Johnson, 2000). When light intensity changes rapidly, regulatory processes at the photosystem level cannot immediately adjust, resulting in a lag in photosystem II (PSII) efficiency and delayed optimization of light harvesting (Kaiser et al., 2015; Pearcy, 1990). The rate of photosynthetic induction varies not only between species but also among cultivars, as demonstrated in soybean (Soleh et al., 2017) and rice (Taniyoshi et al., 2020). Moreover, the duration of shading events can further influence the extent of this delay (Peri et al., 2002). Most of these studies have focused on short-term light treatments and instant physiological responses, often at the leaf level, under controlled light fluctuations. Few have addressed long-term acclimation to such environments or the consequences for integrative traits, including biomass production and yield (Salvatori et al., 2022). When compared to constant shading, intermittent shading can influence morphological traits such as an increase in specific leaf area (SLA), as observed in *Arabidopsis thaliana*, and a reduction in the leaf-to-stem ratio as reported in alfalfa (Vialet-Chabrand et al., 2017; Varella et al., 2011). At the canopy scale, radiation use efficiency (RUE) was increased under constant shading, but could theoretically be reduced under intermittent shading (Varella, 2002). It is therefore necessary to better understand how crops respond to intermittent shading and thereby improve yields predictions in

variable-light environments and optimize multi-layered system designs.

The aim of this study was to (i) quantify the effects of a 38 % reduction of daily light integral on rice development and yield in growth chambers; (ii) compare the impact of constant versus agrivoltaic-like intermittent shading on grain yield, aboveground biomass, and key morphological and physiological traits; (iii) explore genotypic variability in response to either constant shading and intermittent shading using 14 contrasting cultivars, and identify whether specific morphological or physiological traits confer greater tolerance to shade.

## 2. Material and methods

### 2.1. Plant material and growing conditions

An experiment was conducted in 2022 in growth chambers (Aralab Fitoclima 25000HP) at the Abiophen platform in Montpellier, France. Fourteen rice cultivars were used for this study including *Indica* and *Japonica* subspecies, but mainly *Japonica* cultivars, adapted to the Mediterranean climate (Table 1). Seeds were provided by the Genetic Resources Unit of CIRAD, Montpellier.

Seeds were placed in a germination chamber at 28°C for 48 h, then sown on August 18th 2022 in 4.8 L square pots with three seeds per pot.

<caption>**Table 1**</caption>

List of the 14 rice cultivars grown in the experiment. The duration of the phase between sowing and the flowering date (in days) is given for the control treatment (full light). Harvest (in days) represents the duration in days of the phase between sowing and harvest.

<table><thead><tr><th>Accession</th><th>Ref</th><th>Origin</th><th>Taxon</th><th>Flowering (d) in T0</th><th>Harvest (d)</th></tr></thead><tbody><tr><td>Gageron</td><td>V01</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>71</td><td>113</td></tr><tr><td>Arelate</td><td>V02</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>68</td><td>106</td></tr><tr><td>Ronaldo</td><td>V03</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>66</td><td>106</td></tr><tr><td>Gines</td><td>V04</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>68</td><td>109</td></tr><tr><td>Ariete</td><td>V05</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>68</td><td>102</td></tr><tr><td>Arlesienne</td><td>V06</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>65</td><td>109</td></tr><tr><td>Ariana</td><td>V07</td><td>France - Camargue</td><td>Oryza sativa L. subsp. japonica</td><td>100</td><td>138</td></tr><tr><td>Onice</td><td>V08</td><td>Italy</td><td>Oryza sativa L. subsp. japonica</td><td>66</td><td>95</td></tr><tr><td>Sahel 108</td><td>V09</td><td>Senegal</td><td>Oryza sativa L. subsp. indica</td><td>85</td><td>119</td></tr><tr><td>M-202</td><td>V10</td><td>USA</td><td>Oryza sativa L. subsp. japonica</td><td>73</td><td>116</td></tr><tr><td>GIZA 177</td><td>V11</td><td>Egypt</td><td>Oryza sativa L. subsp. japonica</td><td>97</td><td>138</td></tr><tr><td>IR64</td><td>V12</td><td>Philippines</td><td>Oryza sativa L. subsp. indica</td><td>85</td><td>119</td></tr><tr><td>Thaibonnet</td><td>V13</td><td>USA</td><td>Oryza sativa L. subsp. japonica</td><td>74</td><td>116</td></tr><tr><td>Koshihikari</td><td>V14</td><td>Japan</td><td>Oryza sativa L. subsp. japonica</td><td>74</td><td>113</td></tr></tbody></table>
One week after sowing, seedlings were thinned to one plant per pot. The substrate, supplied by Hortiplus (France), had a pH of 4.5 and consisted of 58 % blond peat, 18 % coconut fiber, 10 % perlite, 9 % volcanic sand, and 5 % clay. Fertilization included a recommended NPK application at sowing, incorporating a slow-release NPK fertilizer and a fast-release organic fertilizer at 1.5 g L⁻¹ each (7 g per pot). Thereafter, SPAD values were monitored weekly (see section *Plant traits measurements*). When the mean SPAD value of a genotype × treatment group dropped below 40, an additional fertilization was applied to all five replicates of that group, consisting of a half-dose (3.5 g per pot) of fast-release organic fertilizer. This procedure allowed us to maintain comparable nitrogen status across genotypes and treatments until heading and ensured that SPAD values remained above the 37.5 threshold recommended to sustain adequate leaf N status (Ghosh et al., 2020). Plants were grown in a temperature-controlled greenhouse under natural light for three weeks before being transferred to the growth chambers with different light treatments. Throughout the crop cycle, long days (13 h 20 min with 24 MJ m⁻² d⁻¹) were maintained, except from 35 to 60 days after sowing, when short days (11 h 40 min with 21 MJ m⁻² d⁻¹) were applied to induce early and synchronized flowering. However, this change of photoperiod was ineffective for genotypes V07 and V11, which exhibited very late flowering and an extended growth cycle beyond the experiment schedule. Consequently, they were transferred back from the growth chambers to a greenhouse for grain maturation.

In growth chambers, light was supplied using Philips full-spectrum ceramic metal halide lamps (CDM-TMW 315 W/930 1CT). Twenty days after sowing (DAS), three distinct light treatments were initiated in three separate chambers. The light dynamics in each chamber were manually programmed, a light sensor (Li-190R, Li-Cor Biosciences, Lincoln, NE, USA) was placed at approximately two-thirds of the height of the rice plants to record incident Photosynthetic Photon Flux Density (PPFD). Fig. 1A shows the average PPFD recorded at one-minute intervals throughout the growth cycle for each treatment. For the full light treatment (T0) the diurnal light profile followed a sine-like curve, with light intensity increasing over approximately 2.5 h, reaching a plateau close to 1080 µmol m⁻² s⁻¹ for 8.5 or 7.5 h depending on the photoperiod, and decreasing over another 2.5 h (Fig. 1A). The daily light integral (DLI) for this treatment was 24.0 or 21.1 MJ m⁻² d⁻¹, depending on the photoperiod (Fig. 1B). For the constant shading treatment (T1), the diurnal light profile also followed a sine-like curve, with light intensity increasing over approximately 3.5 h, reaching a plateau close to 720 µmol m⁻² s⁻¹ for 5 or 4 h depending on the photoperiod, and decreasing symmetrically over another 3.5 h (Fig. 1A). The DLI for this treatment

was 14.0 or 13.4 MJ m⁻² d⁻¹, depending on the photoperiod, resulting in a 37.7 % shading rate (Fig. 1A–B). For the intermittent shading treatment (T2), the diurnal time course of light intensity followed the T0 curve shape but interrupted by two shading period where PPFD dropped to 300 µmol m⁻² s⁻¹ from 9 to 11 am and from 1:30–3:30 pm approximately. The DLI of this intermittent shading treatment was 13.9 or 13.2 MJ m⁻² d⁻¹ depending on the photoperiod, resulting in a 38.4 % shading rate (T2) (Fig. 1A–B).

Day/night temperature (relative humidity) were 28°C/22°C (65 %/ 80 %). Average temperature and hygrometry conditions did not fluctuate during the day and were consistent across the three light treatments (Fig. S1). CO₂ concentration was 415 ppm in the 3 growth-chambers. For each treatment, the experimental setup consisted of five tables, each containing 14 pots (one per cultivar) in a randomized arrangement. Pots were rotated twice a week within each table to ensure homogenisation of light exposure and minimize both positional effects and potential neighbour interactions.

## 2.2. Plant traits measurements

Phenological and morphological traits (Table 2A). Plant morphological traits were measured on all individuals about 20 days after flag leaf (N) ligation. Stem height was measured from the stem base to the ligule of the flag leaf of the main culm. Leaf length and maximum width were taken on the penultimate leaf (N-1) and flag leaf (N). The N-1 leaf was sampled after photosynthetic measurements, for 5 cultivars (V01, V03, V06, V08 and V14) and its area was measured using a leaf area meter LI-3000C (Li-Cor Biosciences, Lincoln, NE, USA). The leaves were then oven dried 72 h at 60°C and weighed, to calculate the specific leaf area (SLA). Tillers were counted on each plant every seven to ten days from 28 days to 74 days after sowing (five measurements). Last tillering value was considered as plant's final tiller number. Phenological stages like the ligation of the flag leaf and flowering were recorded every two days for each of the 210 individuals. Flowering was defined as the date when anthesis was visible on approximately 50 % of the spikelets of the main panicle.

Plant photosynthesis-related traits (Table 2B). Rapid ETR-PPFD response curves were obtained using a MINI-PAM-II Photosynthesis Yield Analyzer (Walz, Effeltrich, Germany). Measurements were conducted on the penultimate leaf of the main culm during early grain filling, within a week post-flowering, between 7:00 AM and 1:00 PM. For treatments T0 and T1, measurements were performed under their respective growth chamber conditions. For T2 (intermittent shading),

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** (A) Diurnal patterns of mean photosynthetic photon flux density (PPFD), measured at two-thirds of the canopy height in the three growth chambers programmed for treatments T0 (full light, yellow), T1 (continuous shading, blue), and T2 (intermittent shading, green). Light dynamics are shown for both the regular long-day photoperiod (13 h 20 min) and the short-day period (11 h 40 min), which was applied from 35 to 60 days after sowing to advance and synchronize flowering. (B) Corresponding daily light integrals (DLI, in MJ) for each treatment. The mean reduction in DLI for T1 and T2 relative to T0 is indicated as a percentage.</figcaption></figure>
<caption>**Table 2**</caption>

List of measured traits with their name as used in the text, unit, measurement time and frequency, and the cultivars on which traits were measured.

<table><thead><tr><th>Type</th><th>Variable</th><th>Unit</th><th>Measurement time</th><th>Which cultivars</th></tr></thead><tbody><tr><td rowspan="2">A- Phenological and morphological traits</td><td>Time to flowering</td><td>days</td><td>At 50 % flowering of the main culm panicle</td><td>All</td></tr><tr><td>Morphology</td><td>Number of tillers</td><td>tillers plant<sup>-1</sup></td><td>5 times from 28 days after sowing to heading</td><td>All</td></tr><tr><td rowspan="5">B- Photosynthesis related-traits</td><td>Stem height</td><td>cm</td><td>At early maturation</td><td>All</td></tr><tr><td>N and N-1 leaf length</td><td>cm</td><td>At early maturation</td><td>All</td></tr><tr><td>N and N-1 leaf width</td><td>cm</td><td>At early maturation</td><td>All</td></tr><tr><td>N-1 leaf area</td><td>cm<sup>2</sup></td><td>At early maturation</td><td>V01, V03, V06, V08, V14</td></tr><tr><td>N-1 specific leaf area</td><td>cm<sup>2</sup> g<sup>-1</sup></td><td>At early maturation</td><td>V01, V03, V06, V08, V14</td></tr><tr><td rowspan="3">C-Chlorophyll content</td><td>Maximum electron transport rate (ETR<sub>m</sub>)</td><td>µmoles electrons cm<sup>-2</sup> s<sup>-1</sup></td><td>Following N leaf ligulation</td><td>All</td></tr><tr><td>Efficiency of electron transport (alpha)</td><td>°</td><td></td><td></td></tr><tr><td>Minimum irradiance for ETR saturation (E<sub>k</sub>)</td><td>µmoles photons cm<sup>-2</sup> s<sup>-1</sup></td><td></td><td></td></tr><tr><td rowspan="2">D- Biomass and biomass-related traits</td><td>Vegetative biomass</td><td>g plant<sup>-1</sup></td><td>Once a week from 34 days after sowing to senescence</td><td>All</td></tr><tr><td>Aboveground biomass</td><td>g plant<sup>-1</sup></td><td>Following N leaf ligulation</td><td>All</td></tr><tr><td rowspan="6">Yield and yield components</td><td>Harvest index (HI)</td><td>g g<sup>-1</sup></td><td>At maturity</td><td>All</td></tr><tr><td>Incident radiation use efficiency (iRUE)</td><td>g MJ<sup>-1</sup></td><td>At maturity</td><td>All</td></tr><tr><td>Number of panicles per plant</td><td>Panicles plant<sup>-1</sup></td><td>Over cultivar cycle</td><td>All</td></tr><tr><td>Number of spikelets per panicle (SPP)</td><td>Spikelet panicle<sup>-1</sup></td><td>At maturity</td><td>All</td></tr><tr><td>Grain filling rate</td><td>%</td><td>At maturity</td><td>All</td></tr><tr><td>Thousand kernel weight (TKW)</td><td>g</td><td>At maturity</td><td>All</td></tr><tr><td></td><td>Grain yield</td><td>g plant<sup>-1</sup></td><td>At maturity</td><td>All</td></tr></tbody></table>

plants were temporarily relocated to the T0 chamber to avoid fluctuations in irradiance during measurements. The light response was assessed using a series of actinic light steps: 0, 45, 70, 90, 130, 200, 300, 430, 650, 840, 1190, and 1550 µmol photons m<sup>-2</sup> s<sup>-1</sup>. ETR was calculated as in:

$$ETR = \Phi PSII \times PPFD \times A \times \beta \quad (1)$$

Where ΦPSII the effective quantum yield of PSII, PPFD is the photosynthetic Photon Flux Density (µmol photons m<sup>-2</sup> s<sup>-1</sup>), A is the leaf absorbance (assumed constant at 0.84), and β is the fraction of absorbed light allocated to PSII (assumed as 0.5 for equal distribution between PSII and PSI).

ETR-PPFD response curves were fitted using a rectangular hyperbola model (Ralph and Gademann, 2005) to extract the following parameters: ETR<sub>m</sub>, the maximal electron transport rate at saturation (electrons in µmol m<sup>-2</sup> s<sup>-1</sup>); α, the efficiency of electron transport, or initial slope (alpha, α); and the minimum irradiance for ETR saturation (E<sub>k</sub>, µmol photons m<sup>-2</sup> s<sup>-1</sup>), where:

$$ETR = ETR_m \times \tanh\left(\frac{\alpha \times PPFD}{ETR_m}\right) \quad (2)$$

$$Ek = ETR_m/\alpha \quad (3)$$

This method was chosen because ETR<sub>m</sub> provides an integrative indicator of photosynthetic capacity under high light, reflecting differences in PSII photochemistry and light harvesting when measured under controlled environmental conditions, and alpha represents efficiency under low light. By comparing both, it is possible to assess overall efficiency under both low and high light conditions, which is particularly valuable under fluctuating light. SPAD chlorophyll measurements considered in the analysis were conducted right after fluorometry on the same leaf.

**Chlorophyll content measurements (Table 2C).** Chlorophyll content was estimated using a SPAD meter (SPAD 502Plus, Konica-Minolta) on the last ligulated leaf of the main culm. Values correspond to the mean of three measurements taken at two-thirds of the blade length, following Yuan et al. (2016). For fertilisation monitoring, SPAD was recorded weekly from 35 days after sowing until senescence.

For direct comparison among genotypes and treatments at a common physiological stage, an additional SPAD measurement was taken on the same leaf immediately after the fluorometry measurement, and this value was used in the analyses.

**Biomass, biomass allocation and yield-related traits (Table 2D).** Plants were harvested when all plants of a same genotype reached maturity across the three treatments. Each plant was cut at the base, and the panicles were then separated, counted, and processed individually. Panicles were deseeded, and the rachis was combined with the remaining plant material and oven-dried at 60°C for 72 h and weighed to determine vegetative biomass. Grains were oven-dried at 60°C for 72 h, with empty grains separated from filled ones and both were counted and weighed using a grain counter. The yield components: average spikelet per panicle number (SPP), the ratio of filled grain over the total spikelets and thousand kernel weight (TKW) were assessed subsequently. Grain yield was determined as the total filled grain weight per plant. Incident radiation use efficiency (iRUE) was calculated as the total aboveground biomass (g) produced per unit of incident light integrated over the entire cultivar growth cycle.

## 2.3. Data analysis

Analysis of covariance (ANCOVA) was performed considering genotype (G), total received radiation in MJ (DLI), and a two-level factor for shading intermittence (SI) as covariates. SI described the light dynamics: no intermittence for T0 and T1, and intermittence for T2. The partitioning of light into DLI and SI was done to assess the impact of light quantity independently of light intermittence.

The model was written as followed:

$$y_{ij} \sim G_i + DLI_{ij} + SI_{ij} + G.DLI(ij) + G.SI(ij) \quad (4)$$

where G<sub>i</sub> is genotype i, DLI<sub>ij</sub> is the daily light integral of treatment j, SI<sub>ij</sub> is the shading intermittence regime in treatment j, G.DLI(ij) is the Genotype x DLI interaction, and G.SI(ij) is the Genotype by Shading inter-
mittence regime interaction. For iRUE and ΦPSII, an ANOVA was conducted directly on the treatment effect (a three-level factor: T0, T1, T2), followed by a comparison of the means using Tukey's multiple comparison test at a 5% significance level. All data analyses and figures were computed using R 4.3.3 software (R Core Team, 2024).

## 3. Results

### 3.1. Both constant and intermittent shading modified rice phenology and morphology, resulting in a decrease in biomass and grain yield

Reduction of daily light integral (DLI) by 37%, with constant shading (T1) or 38% with intermittent shading (T2), compared to full light (T0), had a significant effect on almost all measured traits (Table 3). In terms of phenology, shading treatments significantly delayed flowering (P < 0.001, T1: +3 days, T2: +2 days) without delaying maturity (not shown). Regarding morphology, both shading treatments significantly increased stem height (P < 0.001, T1: +1.5 cm,

T2: +3.5 cm), the width and length of the penultimate and flag leaves (P < 0.001), and specific leaf area (P < 0.001), indicating that leaves were larger but thinner (Table 3A). Tiller number per plant was significantly reduced by shading (Table 3A, P < 0.001, T1: -23.3%, T2: -30.5%). Vegetative biomass, panicle number, and grain yield showed significant reductions of around 30% (Table 3B-C). Reductions in grain yield caused by shading treatments was due to a significant reduction in the number of panicles per plant and in the thousand kernel weight (Table 3C). By contrast, the number of spikelets per panicle was not significantly affected by shading (Table 3C). Reduction in grain yield was proportional to the reduction in aboveground biomass, resulting in non-significant changes in harvest index in response to shading (Table 3B).

### 3.2. Intermittent shading reduced biomass and grain yield more than did constant shading

Compared to constant shading, intermittent shading at near-identical

<caption>**Table 3**</caption>

Trait values are given for each of the 3 treatments, T0, T1 and T2 (means ± standard error) with a multivariate analysis (ANCOVA) for (A), phenology and morphology; (B), biomass and biomass allocation; (C), grain yield and yield components. Significance levels are presented for each explanatory variable – Genotype (G), Daily Light Integral (DLI), Shading Intermittence (SI) and interactions terms (cf eq3). Significance level: ns = not significant, \* = p < 0.1, \*\* = p < 0.05, \*\*\* = p < 0.01, \*\*\*\* = p < 0.001. Units for each trait are given in Table 2.

<table><thead><tr><th>Type</th><th>Trait</th><th></th><th>mean</th><th>se</th><th>G</th><th>DLI</th><th>SI</th><th>GxDLI</th><th>GxSI</th></tr></thead><tbody><tr><td rowspan="12">(A)<br>Phenology and<br>morphology</td><td rowspan="3">Time to flowering</td><td>T0</td><td>75.72</td><td>±</td><td>1.38</td><td>***</td><td>***</td><td>*</td><td>*</td><td>ns</td></tr><tr><td>T1</td><td>78.44</td><td>±</td><td>1.62</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>77.48</td><td>±</td><td>1.62</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Number of tillers</td><td>T0</td><td>35.30</td><td>±</td><td>1.75</td><td>***</td><td>***</td><td>**</td><td>***</td><td>*</td></tr><tr><td>T1</td><td>27.09</td><td>±</td><td>1.46</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>24.53</td><td>±</td><td>1.18</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Stem height</td><td>T0</td><td>73.51</td><td>±</td><td>1.11</td><td>***</td><td>***</td><td>**</td><td>*</td><td>ns</td></tr><tr><td>T1</td><td>74.97</td><td>±</td><td>1.14</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>76.92</td><td>±</td><td>1.25</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">N Length</td><td>T0</td><td>18.84</td><td>±</td><td>0.66</td><td>***</td><td>***</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>T1</td><td>21.22</td><td>±</td><td>0.56</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>20.65</td><td>±</td><td>0.53</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">N-1 Length</td><td>T0</td><td>28.93</td><td>±</td><td>0.88</td><td>***</td><td>***</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>T1</td><td>32.67</td><td>±</td><td>0.84</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>32.32</td><td>±</td><td>0.88</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">N Width</td><td>T0</td><td>1.13</td><td>±</td><td>0.03</td><td>***</td><td>***</td><td>*</td><td>ns</td><td>ns</td></tr><tr><td>T1</td><td>1.27</td><td>±</td><td>0.03</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>1.24</td><td>±</td><td>0.03</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">N-1 Width</td><td>T0</td><td>1.01</td><td>±</td><td>0.03</td><td>***</td><td>***</td><td>ns</td><td>*</td><td>ns</td></tr><tr><td>T1</td><td>1.08</td><td>±</td><td>0.03</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>1.11</td><td>±</td><td>0.03</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">N-1 SLA</td><td>T0</td><td>124.76</td><td>±</td><td>3.15</td><td>***</td><td>***</td><td>*</td><td>ns</td><td>ns</td></tr><tr><td>T1</td><td>138.56</td><td>±</td><td>2.84</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>151.57</td><td>±</td><td>3.19</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="12">(B)<br>Biomass<br>and biomass allocation</td><td rowspan="3">Vegetative<br>biomass</td><td>T0</td><td>83.21</td><td>±</td><td>2.65</td><td>***</td><td>***</td><td>**</td><td>***</td><td>ns</td></tr><tr><td>T1</td><td>61.35</td><td>±</td><td>1.92</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>58.43</td><td>±</td><td>1.89</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Aboveground biomass</td><td>T0</td><td>160.8</td><td>±</td><td>3.20</td><td>***</td><td>***</td><td>***</td><td>***</td><td>ns</td></tr><tr><td>T1</td><td>117.0</td><td>±</td><td>2.37</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>110.9</td><td>±</td><td>2.26</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Harvest index (HI)</td><td>T0</td><td>0.48</td><td>±</td><td>0.01</td><td>***</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>T1</td><td>0.47</td><td>±</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>0.47</td><td>±</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="12">(C)<br>Yield<br>and yield components</td><td rowspan="3">Panicle number per plant</td><td>T0</td><td>29.84</td><td>±</td><td>1.01</td><td>***</td><td>***</td><td>ns</td><td>***</td><td>ns</td></tr><tr><td>T1</td><td>21.24</td><td>±</td><td>0.75</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>20.45</td><td>±</td><td>0.61</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Average spikelet per panicle (SPP)</td><td>T0</td><td>104.0</td><td>±</td><td>2.41</td><td>***</td><td>*</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>T1</td><td>107.5</td><td>±</td><td>2.54</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>106.3</td><td>±</td><td>2.46</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Grain filling rate</td><td>T0</td><td>0.918</td><td>±</td><td>0.01</td><td>***</td><td>ns</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>T1</td><td>0.927</td><td>±</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>0.915</td><td>±</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Thousand kernel weight (TKW)</td><td>T0</td><td>28.99</td><td>±</td><td>0.60</td><td>***</td><td>***</td><td>ns</td><td>***</td><td>ns</td></tr><tr><td>T1</td><td>28.01</td><td>±</td><td>0.57</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>27.89</td><td>±</td><td>0.58</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Grain yield</td><td>T0</td><td>77.58</td><td>±</td><td>1.30</td><td>***</td><td>***</td><td>**</td><td>**</td><td>ns</td></tr><tr><td>T1</td><td>55.65</td><td>±</td><td>0.99</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>52.50</td><td>±</td><td>0.93</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
DLI significantly affected several phenological, morphological, biomass and yield-related traits. It caused earlier flowering (P < 0.05, -1d), enhanced stem height (P < 0.05, +2 cm) and reduced the number of tillers per plants (P < 0.05, -9.4 %). Leaf dimensions (length and maximum width) were not affected by intermittence of shading, but SLA significantly increased (P < 0.01, +8.6 %, Table 3A). Grain yield was significantly lower in T2 than in T1 (P < 0.01, -5.7 %). All individual yield components were more reduced in T2 than T1 numerically, but not significantly. This suggests that the significant grain yield reduction under intermittent shading resulted from the cumulative effect of small, non-significant reductions in several yield components.

Tiller number was significantly reduced under intermittent shading (T2 vs. T1), but panicle number showed no significant difference between the two shading treatments, indicating a higher proportion of reproductive tillers in T2 (Table 3A, B). Harvest index was stable between both shading treatments. The reduction in aboveground biomass caused by shading was higher in T2 than T1 (P < 0.001, T1: -27.4 %, T2: -31.3 %). Since the reduction of the daily light integral was similar under both shading treatments (T1: -37.7 %, T2: -38.4 %), the incident-radiation use efficiency (iRUE) increased under both shading treatments relatively to T0, but to a lesser extent in T2 (+11.8 %) than in T1 (+17.3 %) (Fig. 2). This negative effect of intermittent shading on iRUE was highly significant (P < 0.001). Thus, shading intermittence during the day, compared to continuous shading, reduced either light interception, or conversion into biomass, or both.

### 3.3. Intermittence of shading amplified the response of photosynthetic parameters to shading

Both the reduction of daily light integral (shading *per se*, compared to T0) and the shading intermittence (T2 vs. T1) modified the electron transport efficiency. Shading *per se* had a significant effect on the three parameters related to electron transport efficiency: ETR<sub>m</sub> (maximal electron transport rate), alpha (efficiency of the electron transport), and E<sub>k</sub> (minimum irradiance for ETR saturation) (Table 4). Shade increased alpha and decreased ETR<sub>m</sub>. SPAD values were not different between treatments but were highly different among genotypes with a significant genotype x DLI interaction (P < 0.001, Table 4). ETR<sub>m</sub> decreased particularly strongly under shade, whereby intermittent shading (T2) had a significantly greater effect than continuous shading (T1) (P < 0.001). This was also true for the minimum light intensity for

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Incident radiation use efficiency (iRUE) calculated for the three light treatments, T0 (yellow), T1 (blue) and T2 (green). Letters indicate the results of Tuckey's multiple comparison test at 5 % significant level. Genotypes V07 and V11 were excluded from this calculation due to their longer growth cycle, which extended beyond the growth chamber experiment, incident light during maturity therefore not corresponding to the treatment anymore.</figcaption></figure>

saturation (E<sub>k</sub>). Consequently, plants grown under shade attained electron transport saturation at lower light intensities than in full light (T0) and this effect was amplified under intermittent shading (P < 0.001; Table 4). Alpha increased significantly under shade, without significant difference between T1 and T2.

### 3.4. Under intermittent shading, electron transport efficiency decreased more under high than low light across all genotypes

Genotypic effects were highly significant (P < 0.001) for all physiological traits (Table 4). In T0, mean genotypic values for ETR<sub>m</sub> ranged from 59 to 153 µmol<sub>electrons</sub> m<sup>-2</sup> s<sup>-1</sup> and alpha ranged from 0.226 to 0.288. For all treatments, a negative correlation was observed between alpha and ETR<sub>m</sub> across genotypes, indicating a trade-off in photosystem efficiency: genotypes with higher ETR<sub>m</sub> values exhibited lower alpha, and vice versa (Fig. 3A). This trade-off may reflect genotype-specific adaptation to either high or low light environments. Cultivars with high ETR<sub>m</sub> included V03, V05, V06, V08, and V14, while cultivars with high alpha were V09, V12, and, to a lesser extent, V01, V04, and V11 (Fig. 3A). The ETR<sub>m</sub>-alpha correlation was significant for plants grown under full light (T0) and intermittent shading (T2), but not under constant shading (T1) (Fig. 3A). This absence of a significant correlation in T1 was related to genotypic responses observed in T0: cultivars such as V03, V05 and V06, hypothetically showing high-light-adaptation in T0, showed increased alpha and decreased ETR<sub>m</sub> values in T1. This adjustment may convey adaptation to low light. In contrast, the hypothetically shade-adapted cultivars in T0 maintained stable values for both alpha and ETR<sub>m</sub> in T1 (Fig. 3A). In T2, hypothetically shade-adapted cultivars as identified in T0 (e.g., V09 and V12) showed a decrease in both alpha and ETR<sub>m</sub>, while high-light-adapted cultivars such as V02 and V08 did not exhibit similar adjustments.

Superimposing the alpha vs. ETR<sub>m</sub> regression lines for T0, T1, and T2 revealed a shift toward lower ETR<sub>m</sub> under shade (Fig. 3B). This shift was significant for both shading treatments and more pronounced under intermittent shading (T2), indicating a stronger reduction in the maximal (high-light) electron transport capacity (P < 0.001, Table 4; Fig. 3B).

Significant cultivar differences and genotype × shading interactions were observed for both yield (Table 3) and fluorescence parameters (Table 4). Across the cultivars, ETR<sub>m</sub> was consistently negatively correlated with grain yield and with aboveground biomass, with most correlations significant for T0 and T1 treatments (P < 0.05 or P < 0.01), and fewer for T2 (Table 5). In contrast, correlations between alpha and these traits were generally positive, though rarely significant (Table 5). However, neither alpha nor ETR<sub>m</sub> predicted genotypic yield or biomass responses to shading treatments (Table 5, last four columns).

### 3.5. The most productive genotypes under full light were also the most productive under shading conditions

Genotype effects were highly significant (P < 0.001) for all measured phenological, morphological, or yield traits (Table 3) and physiological traits (Table 4), regardless of light treatment. Large trait variation was observed among cultivars even under the T0 control condition (Fig. 4). For example, the mean vegetative biomass across genotypes ranged from 58 to 131 g plant<sup>-1</sup> between V08 and V07, respectively (Fig. 4A) and mean grain yield ranged from 62 to 92 g plant<sup>-1</sup> between V14 and V07 (Fig. 4B). The temperate-japonica type V07 combined high panicle number with large vegetative biomass (Fig. 4A) and had the longest cycle duration (Table 1). Together with the Senegalese indica-type V09, it achieved the highest grain yields (Fig. 4B). Among the 6 most productive cultivars under T0 (V02, V04, V07, V09, V10 and V12), high yield was due to different combinations of yield component traits. V02, V07, V09, and V12 had numerous panicles but relatively low thousand kernel weight (Fig. 4B, D, F). The indica cultivars V09 and V12 exhibited particularly high tillering, which was
<caption>**Table 4**</caption>

Photosynthesis-related trait values per treatment (means ± standard error) and multivariate analysis (ANCOVA) results for chlorophyll content and fluorescence related traits. Significance levels are presented for each explanatory variable – Genotype (G), Daily Light Integral (DLI), Shaing Intermittence (SI) – and interactions terms (cf eq1) Significance level: ns = not significant, \* = p < 0.1, \*\* = p < 0.05, \*\*\* = p < 0.01, \*\*\*\* = p < 0.001.

<table><thead><tr><th>Type</th><th>Trait</th><th>mean</th><th>se</th><th>G</th><th>DLI</th><th>SI</th><th>GxDLI</th><th>GxSI</th></tr></thead><tbody><tr><td rowspan="12">Chlorophyll content and fluorescence</td><td rowspan="3">SPAD</td><td>T0</td><td>51.27</td><td>±</td><td>0.63</td><td>***</td><td>ns</td><td>***</td><td>ns</td></tr><tr><td>T1</td><td>51.20</td><td>±</td><td>0.67</td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>50.87</td><td>±</td><td>0.69</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">alpha</td><td>T0</td><td>0.2473</td><td>±</td><td>0.0016</td><td>***</td><td>**</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>T1</td><td>0.2529</td><td>±</td><td>0.0014</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>0.2503</td><td>±</td><td>0.0014</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">E<sub>k</sub></td><td>T0</td><td>432.5</td><td>±</td><td>15.07</td><td>***</td><td>***</td><td>***</td><td>*</td><td>ns</td></tr><tr><td>T1</td><td>340.8</td><td>±</td><td>11.83</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>291.7</td><td>±</td><td>13.27</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">ETR<sub>m</sub></td><td>T0</td><td>106.0</td><td>±</td><td>3.27</td><td>***</td><td>***</td><td>***</td><td>ns</td><td>ns</td></tr><tr><td>T1</td><td>86.0</td><td>±</td><td>2.89</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>72.5</td><td>±</td><td>3.10</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Relationships between the quantum efficiency alpha (α) and the maximal electron transport rate (ETR<sub>m</sub>). Graphs A shows treatments T0, T1 and T2, whereas graph B shows the 3 regression lines for direct comparison. Points in different colors with codes V01 to V14 represent the different cultivars described in Table 1. Bidirectional error bars indicate standard error of the mean and grey areas indicate the 95 %-confidence interval of linear regressions.</figcaption></figure>

<caption>**Table 5**</caption>

Pearson correlations (R-values) between photosynthetic parameters (α, ETR<sub>m</sub>) and grain yield and aboveground biomass (N = 14 cultivars) for each treatment. Correlation between photosynthetic parameters and response to shade are also presented. Response to shade is calculated as trait value under shade relative to trait value under control conditions (T1/T0 and T2/T0). Treatments: T0, full light; T1, continuous shading; T2, intermittent shading. Values in bold indicate P < 0.05, in bold-underlined P < 0.01.

<table><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Treatment</th><th colspan="3">Grain yield</th><th colspan="3">Above-ground biomass</th><th colspan="2">Grain yield response to shade</th><th colspan="2">Above-ground biomass response to shade</th></tr><tr><th>T0</th><th>T1</th><th>T2</th><th>T0</th><th>T1</th><th>T2</th><th>T1/T0</th><th>T2/T0</th><th>T1/T0</th><th>T2/T0</th></tr></thead><tbody><tr><td rowspan="3">α</td><td>T0</td><td>0.46</td><td><strong>0.54</strong></td><td>0.35</td><td>0.46</td><td><strong>0.59</strong></td><td>0.48</td><td>0.16</td><td>-0.18</td><td>0.37</td><td>0.11</td></tr><tr><td>T1</td><td>0.06</td><td>0.23</td><td>0.15</td><td>0.13</td><td>0.26</td><td>0.25</td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td>0.17</td><td>0.25</td><td>0.26</td><td>0.45</td><td>0.52</td><td>0.53</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">ETR<sub>m</sub></td><td>T0</td><td><strong>-0.69</strong></td><td><strong>-0.56</strong></td><td>-0.46</td><td><strong>-0.67</strong></td><td><strong>-0.70</strong></td><td><strong>-0.61</strong></td><td>0.17</td><td>0.42</td><td>-0.18</td><td>0.15</td></tr><tr><td>T1</td><td><strong>-0.68</strong></td><td><strong>-0.54</strong></td><td><strong>-0.56</strong></td><td><strong>-0.72</strong></td><td><strong>-0.70</strong></td><td><strong>-0.69</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>T2</td><td><strong>-0.63</strong></td><td><strong>-0.63</strong></td><td>-0.53</td><td><strong>-0.70</strong></td><td><strong>-0.78</strong></td><td><strong>-0.70</strong></td><td></td><td></td><td></td><td></td></tr></tbody></table>

linked to a high panicle number (Fig. 4C-D). V10 had fewer panicles but a higher number of spikelets per panicle (Fig. 4B, E). V04 had average panicle number, spikelet number, and thousand kernel weight (Fig. 4B-F). Interactions between genotype and DLI were significant for the traits that were significantly influenced by light treatment such as grain yield,

vegetative biomass, thousand kernel weight, number of tillers and panicle number per plant (Table 3). In contrast, among all traits, only the number of tillers showed a significant interaction between genotype and the effect of intermittent shading (Tables 3 and 4). The high R² values of the correlations across genotypes between trait values in T0
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

**Fig. 4.** Response of biomass and yield-related traits to continuous (T1) and intermittent shading (T2) as compared with no shade (T0) for the 14 rice cultivars. A) Vegetative biomass; B) Grain yield; C) Number of tillers per plant; D) Number of panicles per plant; E) Number of spikelets per panicle; F) Thousand kernel weight. Points in different colors with codes V01 to V14 represent the different cultivars described in Table 1. Symbols differentiate treatments T1 (solid circles) and T2 (empty circles). The shown lines are: The 1:1 diagonal (solid) which indicates an absence of shade effect on the trait and a dotted line which indicate theoretical shade effects proportional to a 38 %-shading. Error bars indicate standard error of the mean.
and their corresponding values in shading treatments, either T1 or T2, suggest that treatment effects were proportional, without marked effects on genotype ranking (Fig. 4A-F). The R² values were higher than 0.9 for vegetative biomass (Fig. 4A), tiller and panicle numbers (Fig. 4C, D), the number of spikelets per panicle (Fig. 4E) and thousand kernel weight (Fig. 4F). The R² values for grain yield were lower for T1 (R²= 0.68) and T2 (R²= 0.78) (Fig. 4B). Thus, the most productive cultivars under full light (T0) remained among the most productive under shading conditions (Fig. 4B).

# 4. Discussion

## 4.1. Shading incurs sub-proportional losses in yield and biomass

Shading reduced tillering and therefore the number of panicles per plant, consistent with Yang et al., (2007). Together with a slight reduction in thousand-kernel weight, this explained the observed yield loss. However, the decreases in grain yield and aboveground biomass at maturity were sub-proportional to the reduction in available light under both shading treatments.

Biomass production depends on radiation use efficiency and thus may impact yield (Hammer et al., 2010; Sinclair and Muchow, 1999). For plants that are not forming a homogenous canopy like plants in pot experiments with mixed genotypes, estimating actual RUE is difficult (Cabrera-Bosquet et al., 2016). RUE accounts for the conversion of intercepted light, which is a fraction of incident light, into biomass. In contrast, incident radiation use efficiency (iRUE) expresses biomass produced per unit of incident light received. While conceptually linked to RUE, iRUE serves as a simpler metric, not accounting for differences in light interception among treatments. In this study, iRUE increased under shading while overall yield and biomass declined. Increases in RUE under moderate shading have been reported previously (Healey et al., 1998; Marrou et al., 2013b). From an energy-balance point of view, this is of interest in agrivoltaics: diverting some of the solar radiation for electricity generation incurs a sub-proportional cost to the crop in terms of photosynthesis (Dupraz, 2023; Marrou et al., 2013c). This increase in incident radiation use efficiency in response to shade can be attributed to changes in morphology such as larger and thinner leaves increasing area for light interception, as well as photosynthetic adjustments improving light harvesting and electron transport under

low light (H. Li et al., 2010; Wang et al., 2015). The increase in iRUE under shading, can also be attributed to the convex shape of the photosynthesis response curve to radiation, which is a non-rectangular hyperbola (Thornley and Johnson, 1990; Fig. 5A). Under shading conditions, such as those applied in the constant shading treatment (T1) of this experiment, PPFD levels typically fall within the range where photosynthetic assimilation is rapidly increasing without reaching saturation (In T1, max PPDF = 720µmol m⁻² s⁻¹). As a result, shaded plants did not experience saturating light, which enhanced their radiation use efficiency. This behaviour is pronounced in C3 plants like rice, but much less in C4 crops like maize that do not show light saturation within the natural range of solar radiation (Mahendra et al., 1974). However, because maximum PPFD in the growth chambers never exceeded 1080 µmol m⁻² s⁻¹, the plants in T0 did not experience the saturating irradiance levels typical of field conditions, where midday light intensity can reach around 2000 µmol m⁻² s⁻¹ (Ramos-Fuentes et al., 2023). As a consequence, the negative effects of shading are likely overestimated compared to natural high-light environments.

## 4.2. Shading intermittence further reduces yield and enhances shade avoidance responses

With a fine control of growth-chambers, the same reduction of daily light integral was obtained with different light partition during the day: based on an approximately bell-shaped diurnal light distribution, a constant proportional reduction (T1) and an intermittent fluctuating reduction (T2). This setup enabled studying the effect of intermittence alone (T2 vs T1). In terms of morphological differences plants grown under intermittent shading compared to constant shading had increased stem elongation, reduced tillering, and higher specific leaf area (SLA), as also reported by (Vialet-Chabrand et al., 2017) for *Arabidopsis thaliana* under fluctuating light conditions. All these morphological changes are indicative of the shade avoidance syndrome (SAS). Shade tolerance strategies are associated with suppression of the SAS (Gommers et al., 2013; Martinez-Garcia and Rodriguez-Concepcion, 2023) and/or enhanced photosystem efficiency (Wang et al., 2015). Given the amplification of the SAS and the significantly altered photosystem efficiency across all cultivars under intermittent shading, it appears that shading intermittence impairs the expression of shade tolerance in plants. Therefore, when discussing shading and shade tolerance, it is

<figure><img src="image_13.png" /><figcaption>**Fig. 5.** (A) Net CO₂ assimilation response of the rice canopy to photosynthetically photon flux density (PPFD) at 41 (solid lines) and 64 (dashed lines) days after sowing (DAS), adapted from Dingkuhn et al. (1990). (B) Theoretical daily net CO₂ assimilation integrated over time under the three light treatments in the growth chambers: T0 - control (yellow), T1 - constant shading (blue), T2 - intermittent shading (green), at both 41 (solid lines) and 64 (dashed lines) DAS. Photorespiration was not accounted for, and any negative assimilation values were replaced with zero.</figcaption></figure>
essential to consider the type of shading. The amplification of SAS under
shading could be related to intermittent exposure to a highly reduced
absolute quantity of blue light because the intermittent shading phases
were more intense than the constant shading). The absolute amount of
blue light decreased with light intensity, since the ratio of blue light to
total light intensity remains constant (Fig. S3). Blue light is reported to
play a critical role in de-etiolation processes (Christophe et al., 2006;
Cope and Bugbee, 2013; Pattaro et al., 2024).

Biomass and grain yield decreased under intermittent shading con-
ditions, resulting in a substantially lower conversion efficiency of the
incident light than under constant shading (-4.6 %, Fig. 3). The differ-
ences in incident radiation use efficiency between T1 (constant shading)
and T2 (intermittent shading) can be partly explained by the hyperbolic
light response curve of photosynthesis. Intermittent shading (T2) had
two distinct phases: one at low light intensity falling within the linear
segment of the light response curve, and another at high light intensity,
where assimilation is limited by diminishing returns when approaching
light saturation. As a result, the average assimilation rate across low and
high light levels in T2 is lower than the assimilation rate at intermediate
light intensity, leading to a reduction in daily assimilation under inter-
mittent shading. To estimate how intermittent light may impact carbon
assimilation, we calculated the theoretical diurnal carbon assimilation
by using empirical logarithmic A\_canopy vs. PPFD relationships reported
by Dingkuhn et al., (1990) for two rice developmental stages (41 and 64
days after sowing), and the measured radiation under the three light
treatments in this study (Fig. 5B). Calculated diurnal cumulative inte-
grated assimilation was reduced under intermittent (T2) compared to
constant (T1) shading, by 3 % at 41 days after sowing and by 10 % at 64
days after sowing. Our observed aboveground biomass and yield
decrease at maturity in intermittent vs. constant shading falls within this
range, with -5.3 % and -5.7 % on average, respectively (Table 3). This
finding aligns with the results of a previous study by Varella et al.
(2011), in which the effects of intermittent and constant shading on
alfalfa were compared. At similar DLI, the weighted leaf net photosyn-
thetic rate, accounting for both sun and shade exposure durations, was
lower under intermittent shading compared to leaf net photosynthetic
rate under constant shading. Overall, the additional impact of shading
intermittence on biomass and yield remained modest, generally in the
range of 5–10 % percent. This moderate amplitude is consistent with the
fact that daily light quantity was the dominant driver of shading effects
in this experiment and intermittent shading consequences should
therefore be interpreted as second-order effects.

4.3. *Intermittent shading likely reduces photosystem II efficiency and photosynthetic efficiency*

The estimated daily theoretical assimilation losses of 3–10 % caused
by intermittence of shading (Fig. 5B) do not account for the observed
physiological adjustments, which likely further amplified growth dif-
ferences between constant and intermittent shading. ETR<sub>m</sub> is known to
be correlated with photosynthetic rate at light saturation (Perera-Castro
and Flexas, 2023; Perez et al., 2024), and the lower values observed
under intermittent shading suggest a reduction in photosynthetic ca-
cacity, possibly due to limitations in Jmax (Farquhar et al., 1980).
However, ETR only reflects the electron flow in PSII and does not cap-
ture limitations of carbon assimilation further downstream. Several
other processes may occur after electrons transfer to plastoquinone and
can also be influenced by shading. These include cyclic electron trans-
port (Shikanai, 2014; Yamori et al., 2011), photorespiration (Busch,
2020), and mitochondrial respiration (Noguchi and Yoshida, 2008).
These pathways divert energy and can limit net CO<sub>2</sub> gain. In addition,
photoprotective mechanisms such as non-photochemical quenching
(Murchie and Niyogi, 2011) or photoinhibition (Genty et al., 1989) may
further reduce photosynthetic efficiency under fluctuating light condi-
tions, particularly during intermittent shading.

In this study, the electron transport rate (ETR) values were calculated

based on the standard assumptions of fixed leaf absorptance (0.84) and
equal energy distribution between photosystems (0.5), which are
commonly used in pulse-amplitude-modulated (PAM) fluorometry
(Genty et al., 1989). As measurements were performed under identical
PPFD levels across treatments, the observed differences in ETR and its
derived parameters (alpha, ETR<sub>m</sub>, and E<sub>k</sub>) can be attributed primarily to
changes in the quantum yield of PSII photochemistry (ΦPSII). As ex-
pected, ΦPSII differed significantly between treatments (see Fig. S2),
with significantly lower values under intermittent shading. The reduc-
tion in ETR and the variation in its related parameters were thus prob-
ably driven by a decline in ΦPSII. ΦPSII can be influenced by several
factors, including the structure and composition of the light-harvesting
complex (LHC), pigment content, and the activation of photo-
protective mechanisms such as non-photochemical quenching (NPQ)
(Kalaji et al., 2017; Murchie and Horton, 1997). Changes in antenna
size, the chlorophyll a:b ratio or xanthophyll cycle activity can reduce
ΦPSII by altering the balance between photochemistry and thermal
energy dissipation (Demmig-Adams, W. W. 1992; Ruban, 2016). Lastly,
reductions in ΦPSII may also be associated with changes in leaf optical
properties and PSI/PSII stoichiometry, which alter light absorptance and
excitation energy distribution. This can potentially challenge the fixed
parameters used in the presented ETR estimation (Baltzer and Thomas,
2005; Wientjes et al., 2017). Vialet-Chabrand et al. (2017) demonstrated
a decrease in absorptance and the chlorophyll a:b ratio in plants culti-
vated under fluctuating light conditions. The decrease in efficiency
under high light and the potential role of photoprotective mechanisms
suggest that intermittent shading may involve not just shading stress,
but also high-light induced stress from the sunlit periods.

4.4. *The ranking of genotypes for the main production traits was consistent among the treatments*

Genotypic effects on key production traits remained consistent
across all light treatments. These findings are consistent with Perez
et al., (2024) who also reported consistent yield ranking among rice
varieties across three light treatments. Genotypes with greater tillering
capacity, such as the two *indica* cultivars studied here, may be
pre-adapted to shaded environments due to enhanced self-shading,
which convey acclimation to low-light conditions (Almeida et al.,
2022). However, this was not the case here. *Indica* cultivars exhibited
yield reductions similar to *japonica* cultivars under both continuous
(29 % vs. 28 %) and intermittent (33 % vs. 32 %) shading treatments.
Across all genotypes, no significant correlations were found between
morphological or photosynthetic trait variations and shade effects on
yield or aboveground biomass. This indicates that none of the studied
traits can be used as proxies for shade adaptation. Photosynthetic
inertia, as described by Kaiser et al. (2015), was not considered here
because of the limited frequency of light changes in our setup (only two
drops in light intensity per day in T2). There is known genotypic vari-
ation in the lag of photosynthetic induction (Acevedo-Siaca et al., 2021).
If light intensity fluctuations had occurred more frequently, as in sun-
fleck regimes, this could potentially affect performance.

Based on this experiment, we were unable to identify either a specific
ideotype suited to intermittent shading or a trait associated with
improved performance under such conditions. While no single genotype
outperformed others under all light regimes, those with higher pro-
ductivity under full light (T0) generally maintained better performance
across shading treatments.

4.5. *Intermittent shading and spectral composition in the field, a challenge for crop modelling in agrivoltaic systems*

Given the increasing importance of multi-layered canopy systems,
including agrivoltaics, improving their representation in crop models
has become essential. However, the current common crop models do not
specifically consider complex diurnal dynamics of light. Many crop
models estimate daily biomass accumulation based on the daily light integral (DLI) and a radiation use efficiency (RUE) parameter, assuming a constant light environment during the day (Boote et al., 2013; Potenza et al., 2022; Vezy et al., 2023). These models might thus over-estimate crop production under strong, intermittent shading. The present results suggest that intermittent shading effects require further study, including in the field, to make their impact on crops predictable.

Under field conditions, additional sources of shading such as cloud cover, atmospheric haze and plant movement can introduce more light fluctuations known as sunflecks or shadeflecks, influencing crop photosynthesis and development depending on their intensity and frequency (Kaiser et al., 2018; Smith and Berry, 2013). They would add to the shading intermittence used in this study, which approximated only the diurnally migrating shade of structures such as solar panels only. Furthermore, light quality plays an important role in the way plants respond, particularly in terms of morphology and phenology (Goins et al., 1997). The results presented here are specific to the light spectrum used in the growth chambers, where light quality remained consistent across different photosynthetic photon flux densities (see Fig. S3). However, light quality is altered further under agrivoltaic systems, particularly through changes in the diffuse-to-direct radiation ratio (Rosati et al., 2024). While direct radiation is greatly reduced, diffuse radiation remains relatively unaffected (Ma Lu et al., 2024). Nevertheless, numerous studies have shown that an increased proportion of diffuse radiation can enhance light-use efficiency and CO₂ uptake across various ecosystems, including forests and winter wheat systems (Cheng et al., 2015; Gu et al., 2002; Still et al., 2009). An increased diffuse-to-direct radiation ratio also enhances the proportion of blue light, as more light is received from the blue-rich portion of the sky (Rosati et al., 2024). Increasing the proportion of indirect and blue light in the transmitted spectrum could potentially reduce the theoretical gap between constant and intermittent shading. It could also reduce the amplification of the shade-avoidance syndrome observed under intermittent shading (Christophe et al., 2006; Pattaro et al., 2024).

## 5. Conclusion

All genotypes exhibited sub-proportional yield losses under 37–38 % shading. Intermittent shading, compared to constant shading, induced greater shade avoidance responses and decreased incident radiation use efficiency (iRUE), resulting in further reductions in biomass and grain yield. This effect was consistent across all cultivars, with no genotype-specific responses to intermittent shading. No, traits could be identified as reliable predictors of shade tolerance, or specifically tolerance to intermittent shading. These findings highlight the need to improve crop modeling for fluctuating light conditions, particularly for agrivoltaic systems, where the influence of dynamic light environments on photosynthesis remains largely unexplored. Future research should therefore focus on incorporating the physiological and agronomic effects of diurnal light dynamics into crop models to improve the prediction of yield outcomes in such systems.

### CRediT authorship contribution statement

**Boudon Frederic:** Writing – review & editing. **Lescroart Laura:** Writing – review & editing, Writing – original draft, Investigation, Formal analysis, Data curation. **Perez Raphael P. A.:** Writing – review & editing, Investigation, Conceptualization. **Dingkuhn Michael:** Writing – review & editing, Investigation, Funding acquisition, Conceptualization. **Granier Christine:** Writing – review & editing.

### Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT and DeepL in order to improve language. After using these tools, the author

(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

## Funding

This work was conducted within the framework of the ADELI project, which focuses on research in rice agrivoltaics in the French Camargue region. The ADELI project is led by EDF Power Solutions under a consortium agreement with CIRAD (UMR AGAP Institute), INRAE (UR P3F), and RemTec, and funded by the French Agency for Ecological Transition (ADEME).

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We are most grateful to the CIRAD Ecophysiology and plant growing platforms for their technical support. We thank the two anonymous reviewers for their helpful constructive comments to improve this manuscript. thank the Montpellier GAMET Biological Resource Centre (Centre de Ressource Centre de Ressources Biologiques des 'Graines Adaptées aux conditions Méditerranéennes et Tropicales', ARCAD Montpellier – UMR AGAP Institute de Montpellier) for providing the germplasm.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.envexpbot.2025.106301.

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