Next Article in Journal
Effects of Managed Disturbances on Plant Community Structure and Nutritional Function in the Tropical Savanna Habitat of the Endangered Eld’s Deer (Rucervus eldii)
Previous Article in Journal
Evidence of the Role of Plant Growth-Promoting Bacteria in Mitigating N2O Emissions from Maize Cultivation
Previous Article in Special Issue
Mechanistic Insights into Silicon-Enhanced Cadmium Detoxification in Rice: A Spatiotemporal Perspective
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 12
10.3390/agronomy15122855
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Rice Responds to Different Light Conditions by Adjusting Leaf Phenotypic and Panicle Traits to Optimize Shade Tolerance Stability and Yield

by
Shihui Yang
,
Lingyi Li
,
Guangyuan Wang
,
Yan Liu
,
Ying Kong
,
Xianghui Li
,
Yufei Liu
,
Zhensheng Lei
,
Shareef Gul
,
Guanghua He
* and
Hesheng Yao
*
Rice Research Institute, College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(12), 2855; https://doi.org/10.3390/agronomy15122855
Submission received: 6 November 2025 / Revised: 6 December 2025 / Accepted: 8 December 2025 / Published: 12 December 2025
(This article belongs to the Special Issue Rice Cultivation and Physiology)
Browse Figures
Versions Notes

Abstract

Prolonged low-light stress during growth significantly reduces rice yield in southwest China. In order to systematically study the dynamic response of rice to long-term shading, field experiments were conducted in Chongqing, China, from 2021 to 2022, investigating the effects of 50% and 75% shading from the seedling to heading stage on morphological characteristics, physiological traits, and yield formation in 12 rice cultivars. The results showed that shading reduced tiller number, leaf mass per area, total dry mass, leaf area index, panicle number, seed-setting rate, and yield. Meanwhile, rice acclimated to low light by increasing plant height, leaf chlorophyll content, and leaf-total mass ratio. In particular, leaf width in low-light treatments was narrower under short-term shading but became wider under long-term shading compared to natural light. Moreover, under 50% shading condition, rice optimized panicle structure by increasing grain number per panicle and primary and secondary branch numbers to compensate for adverse effects. Cultivars, including Le you 918 and Shen 9 you 28, exhibited high yield and strong shade tolerance. Overall, rice acclimates to low light through the synergistic interactions of various traits, with leaf phenotypic adjustments and panicle structure optimization being crucial for improving yield under low light.

1. Introduction

Since 2000, global dimming has been predominantly attributed to cloud cover, with aerosols playing a secondary role [1,2]. In southwest China, particularly in the mountainous Chongqing region, complex topography facilitates persistent cloud and fog formation, making it a region with solar radiation deficits [3,4]. According to the criteria of the National Energy Administration, Chongqing is classified as a solar energy resource-poor area, with approximately 104 foggy days annually and an annual sunshine of 1000 to 1400 h.
As a photophilous species, rice requires substantial light exposure for optimal growth and development. Insufficient light directly impairs photosynthetic efficiency and energy accumulation in rice plants [5]. Rice cultivation experiences heterogeneous shading patterns across growth stages due to climatic, geographical, and seasonal variations [6]. Research has shown that rice is highly sensitive to light during the tillering stage, and low-light stress significantly diminishes tiller number and the photosynthetic efficiency of rice, ultimately affecting biomass accumulation [7]. During the panicle initiation stage, light deficiency disrupts floral development, leading to a reduced seed-setting rate [8]. The heading and flowering stages are periods when rice has a high demand for light, and low-light stress can cause substantial yield losses due to impaired pollination [9]. The grain-filling stage was a critical period that determined the yield and quality of rice, and low-light stress interferes with starch biosynthesis and nutrient partitioning, elevating chalkiness while degrading rice quality [10].
Rice employs multifaceted phenotypic plasticity to acclimate to low-light environments, including tiller modulation, leaf morphological acclimatization, and assimilate reallocation [11]. These compensatory mechanisms are essential for sustaining photosynthetic performance and yield stability. Under low-light stress, rice reduced the number of ineffective tillers, thereby reallocating more resources to the growth of productive tillers [12]. This resource reallocation optimizes light use efficiency and biomass partitioning [13]. Leaf mass per area (LMA) adjustments have been demonstrated to mitigate photosynthetic and yield penalties under low-light conditions [14,15]. Furthermore, rice tends to allocate more biomass to the above-ground leaves under low-light stress to enhance light interception capture [11]. To cope with low-light conditions, rice also adjusts leaf physiological properties, such as increasing chlorophyll content to capture more light [16].
In southwest China, rice production is subjected to long-term low-light stress, particularly during the early vegetative stages that critically influence yield formation [17]. However, current research mostly focuses on a single growth stage or short-term shading, lacking systematic studies on the dynamic responses of rice to long-term shading. The objective of this study is to explore how rice adjusts leaf phenotypic and panicle traits to optimize shade tolerance stability and yield under long-term shading. This work aims to provide a comprehensive understanding of rice’s acclimatization mechanisms to long-term low light and offer scientific support for optimizing rice production in southwest China.

2. Materials and Methods

2.1. Experimental Design and Field Management

Field experiments were conducted at the Rice Research Institute of Southwest University in Beibei, Chongqing, China (29°45′28″ N, 106°22′56″ E) in 2021 and 2022. The experimental site features a subtropical monsoon humid climate with frequent cloudy and foggy weather, resulting in limited annual duration of sunshine at the early growth stage in rice. Meteorological data are shown in Figure 1. The precipitation was higher in 2021 than that in 2022. The daily maximum temperatures often exceeded 35 °C during July and August in 2022. During the rice growing season, both sunshine duration and total solar radiation peaked from June to August, with values in 2022 being significantly higher than those in 2021, indicating more favorable light conditions (Table A1).
The experiment adopted a two-factor split-plot design. Light treatment was the main factor and cultivar was the sub-factor. Three light treatments—natural light (L1), 50% shading (L2, photosynthetic photon flux density reduced by 50%), and 75% shading (L3, reduced by 75%)—were applied using sun-shading nets. Photosynthetic photon flux density was measured with a LI-191R linear quantum sensor (LI-COR Biosciences, Lincoln, NE, USA) with 12 rice cultivars in this experiment (Table 1). Both the main factor and the sub-factor were arranged in a completely randomized design with three replications. Seeds were sown in a nursery in mid-April. Then, the seedlings were transplanted into fields after 30 days. Shading treatments were applied from the seedling stage (15 days after transplanting) to the heading stage (85 days after transplanting) in both years. The sun-shading nets were suspended 3 m above ground level. The row spacing was 26.7 cm. The intra-row spacing between rice hills was 16.7 cm. Each plot, with an area of 4.46 m2 (2.67 m × 1.67 m), contained 100 plants arranged in 10 rows. Compound fertilizer (N:P2O5:K2O = 15:7:23; total nutrients ≥45%) was applied at 150 kg hm−2 before transplanting and 60 kg hm−2 at the heading stage. Field management practices followed local rice production protocols.
Three individual plants were randomly sampled from each subplot (cultivar × light combination, three replicate subplots) at 10, 20, and 40 days after shading; every plant was measured for morphological characteristics, physiological parameters, and dry mass in sequence.

2.2. Morphological Characteristics and Physiological Measurements

Plant height and tiller number were recorded for all sampled plants within the experimental plots. The first fully expanded leaf from the top of each plant was taken, and four segments were cut from the central part of the blade. Two segments were used to determine leaf mass per area (LMA) and the other two for pigment content. The leaf width was measured at the widest part with the vernier caliper. After drying at 85 °C to constant weight, LMA was computed as the ratio of dry mass to segment area:
LMA (g m−2) = leaf dry mass (g)/leaf area (m2)
For pigment extraction, leaf samples were immersed in 80% acetone and stored in darkness for 48 h, and absorbance values at 470 nm, 645 nm, and 663 nm were recorded using a spectrophotometer (UV2600, Tianmei, Shanghai, China). Chlorophyll (Chl) and carotenoid (Car) concentrations were calculated according to Lichtenthaler and Wellburn [18]:
Chl a (mg L−1) = 12.21 A663 − 2.81 A645
Chl b (mg L−1) = 20.13 A645 − 5.03 A663
Car (mg L−1) = (1000 A470 − 3.27 Chl a − 104 Chl b)/229
Contents per unit leaf area (mg m−2) were obtained by multiplying the concentration by the extract volume (V) and dividing by the segment area (A):
Chl (a + b) (mg m−2) = (Chl a + Chl b) × V/A
Car (mg m−2) = Car × V/A
The leaf area per plant was estimated by dividing total dry mass by LMA. Leaf area index (LAI) was calculated as:
LAI = leaf area per plant (m2)/ground area per plant (m2)

2.3. Dry Mass Measurement

The root, leaf, stem, and panicle of each plant were separated into sample bags, and then these sample bags were placed in an oven (Hengyue, Shanghai, China) to dry at 85 °C until they reached constant weight. The following ratios were calculated:
Leaf-total mass ratio = leaf dry mass/total dry mass
Root-shoot ratio = root dry mass/shoot dry mass

2.4. Yield Component Measurement

Five plants per plot were used for yield component evaluation, including panicle number, panicle length, number of primary branches, number of secondary branches, grain number per panicle, seed-setting rate, 1000-grain weight, and yield.

2.5. Shade Tolerance Coefficients

Shade tolerance coefficient (STC) was calculated as:
STC (%) = (shading treatment value/natural light value) × 100

2.6. Statistical Analysis

Statistical analysis was conducted with SPSS 23.0 (SPSS Inc., Chicago, IL, USA) to perform analysis of variance (ANOVA) followed by post hoc Duncan test. GraphPad Prism 9.5.0 (GraphPad Software, San Diego, CA, USA) was used to draw the figures. RStudio (RStudio 2024.12.1+563, PBC, Boston, MA, USA) was used to draw the GGE biplot.

3. Results

3.1. The Dynamic Response of Morphological Characteristics, Physiological Traits, Dry Mass Accumulation, and Yield of Rice to Low Light

The morphological characteristics, physiological traits, dry mass, and yield of rice were influenced by the comprehensive effects of cultivar, light, and year (Table A3, Table A4, Table A5 and Table A6). Statistical analyses showed that cultivar, light, and year all had significant effects on every measured trait (p < 0.05). Significant interactions were observed among these three factors as well as between light and cultivar.
Shading treatments had a significant impact on the morphological characteristics of rice (Figure 2 and Figure 3). After 10 days of shading, the plant height significantly increased, with no significant difference between the L2 and L3 treatments. However, long-term shading (40 days) resulted in distinct treatment rankings, with L2 > L3 > L1 (Figure 2A,B). The tiller number showed rapid sensitivity to shading, significantly decreasing after 10 days of shading (Figure 2C,D). This inhibitory effect persisted throughout the entire growth cycle. Leaf morphology exhibited dynamic responses to shading (Figure 3). After 10 days of shading, the leaf width of rice was significantly decreased. However, at 20 days of shading, the leaf widths in the shading treatments increased rapidly, nearly matching and even marginally exceeding values observed under natural light (Figure 3B). After 40 days of shading, the leaf widths in the shading treatments were significantly greater than those in natural light, with no differentiation between the L2 and L3 treatments. The responses of leaf mass per area (LMA) and leaf area index (LAI) to shading were consistent with the response of tiller number, declining as the light intensity decreased, in the order of L1 > L2 > L3 (Figure 3C–F).
Shading significantly affected the physiological traits of rice, particularly the leaf chlorophyll content (Figure 4). After 10 days of shading, the chlorophyll content (Chl(a + b)) increased significantly, with no significant differences observed between L2 and L3 treatments. After 40 days of shading, the leaf chlorophyll content showed significant differences among treatments, in the order of L2 > L3 > L1. The Car content increased significantly under shading, following the same upward trend as Chl(a + b) (Figure A1).
Shading significantly suppressed the accumulation of dry mass in rice (Figure 5) and altered the allocation of dry mass (Figure 6). After 10 days of shading, the total dry mass of rice decreased significantly. As the duration of shading increased, the inhibitory effect of shading on total dry mass became more pronounced, with the order of treatments being L1 > L2 > L3 (Figure 5). After 10 days of shading, the leaf-total mass ratio in rice increased significantly. Under long-term shading, the differences among treatments in this ratio increased, following the order L1 < L2 < L3 (Figure 6A,B). Additionally, shading led to a decrease in the root-shoot ratio of rice, with the most pronounced effect observed after 40 days of shading (Figure 6C,D).
Shading significantly affected yield traits (Table 2). The results indicated that as the light intensity decreased, the reductions in panicle number, seed-setting rate, and yield became more pronounced than those under natural light. The number of primary branches, the number of secondary branches, and the grain number per panicle initially increased and then declined as the light intensity decreased. In 2021, light conditions had no significant effect on 1000-grain weight; however, in 2022, 1000-grain weight significantly declined as the light intensity decreased. Additionally, compared with L1, the panicle length in L2 showed an increasing trend.

3.2. Analysis of Shade Tolerance Coefficients for Agronomic Traits

The shade tolerance coefficients (STCs) of various rice traits are shown in Figure 7. For different indicators, the STC trends under 50% and 75% shading conditions were consistent. Under L2 and L3 treatments, the STCs for tiller number, total dry mass, leaf area index (LAI), and leaf mass per area (LMA) were all below 100% after 10, 20, and 40 days of shading, which were 38.1–62.5%, 37.9–72.3%, 59.4–89.8%, and 60.3–92.8%, respectively. Conversely, the STCs for leaf-total mass ratio, leaf chlorophyll content (Chl(a + b)), and plant height were all above 100% and increased under long-term shading. Specifically, the STCs for leaf-total mass ratio were 103.3–112.1%, 112.5–130.8%, and 114.2–153.2% after 10, 20, and 40 days of shading, respectively. The STCs for Chl(a + b) were 108.1–117.9%, 105.3–135.4%, and 108.2–145.8% after 10, 20, and 40 days of shading, respectively. The STCs for plant height were 101.9–112.7%, 113.8–116.1%, and 106.6–124.3% after 10, 20, and 40 days of shading, respectively. Except for the 10 days of shading in 2021, the STC for the root-shoot ratio was above 100%, while it was below 100% in other cases. The STCs for leaf width were 81.3–89.9%, 95.4–106.9%, and 109.8–124.5% after 10, 20, and 40 days of shading, respectively.
The analysis of shade tolerance coefficients (STCs) for the yield traits of rice revealed that the STCs for the number of primary branches, number of secondary branches, and grain number per panicle exceeded 100% under L2 treatment in 2021 (Figure 8) and approached 120% in 2022. However, under L3 treatment, these three indicators were below or close to 100%. In contrast, the STCs for yield and panicle number in 2021 and 2022 were both relatively low (STC < 80%), with the STC ranges for yield and panicle number being 39.1–70.6% and 55.6–72.6%, respectively, while the STCs for seed-setting rate and 1000-grain weight ranged between 80% and 100%. Compared to 2021, shading had a more pronounced impact on the yield traits of rice in 2022.
Correlation analysis was conducted on the STCs. The results showed that the STC for total dry mass was significantly and positively correlated with the STC for yield (Figure 9). In the heatmap analysis of STCs for rice phenotypic traits (Figure 10), the STC for total dry mass exhibited positive correlations with the STCs for plant height, tiller number, LAI, and LMA, but a negative correlation with the STC for the leaf-total mass ratio. Additionally, the STC for the leaf-total mass ratio was positively correlated with the STCs for leaf width and Chl(a + b), and the STC for LAI was positively correlated with the STCs for plant height and tiller number. In the heatmap analysis of STCs for yield traits (Figure 11), among the yield components, the STCs for 1000-grain weight and panicle length showed relatively low correlations with the STC for yield. In contrast, the STCs for other factors, such as number of primary branches, number of secondary branches, and grain number per panicle, were highly positively correlated with the STC for yield. Notably, the STC for panicle number had the closest relationship with the STC for yield.

3.3. Differences in Shade Tolerance Among Rice Cultivars

In this study, there were differences in yield STC among rice cultivars (Figure 12). Through a two-year field experiment, we found that under the 50% shading condition, the majority of tested rice cultivars had STCs exceeding 60%. Under the 75% shading condition, the rice cultivars with STCs greater than 40% in 2021 were numbers 4, 5, 6, 7, 8, 9, and 11, while in 2022, they were numbers 5, 6, 7, 8, 10, 11, and 12. Cultivars 1 and 2 exhibited relatively low STCs under both the 50% and 75% shading conditions. Further analysis was conducted on the actual yield of different rice cultivars under different light conditions. The results indicated that rice yield decreased significantly as the light intensity decreased (Figure 13). In 2021, the yield ranges of rice under L1, L2, and L3 treatments were 6346–8681 kg hm−2, 4037–6642 kg hm−2, and 1904–5167 kg hm−2, respectively. Under the L2 treatment, rice cultivars with material numbers 3, 4, 7, 9, and 10 exhibited relatively higher yields. Under the L3 treatment in 2021, cultivars with material numbers 4, 5, 7, and 11 achieved higher yield levels (Figure 13A). In 2022, the yield ranges of rice under L1, L2, and L3 treatments were 7034–9776 kg hm−2, 4816–7600 kg hm−2, and 2611–4348 kg hm−2, respectively. Under the L2 treatment, cultivars 3, 6, 7, 10, and 11 demonstrated superior yield performance. Under the L3 treatment, cultivars 6, 7, and 11 exhibited notably higher yields (Figure 13B, Table A2).
The GGE biplot revealed the patterns of genotype-by-environment (G × E) interactions, providing an important basis for cultivar evaluation and environmental acclimatization analysis. In this study, light conditions were used as the environmental variable, and yield was considered as the response factor. The results showed that in 2021, the three light environments were grouped together, with cultivars 4, 7, 9, and 10 exhibiting superior overall yield performance in these environments (Figure 14A). Specifically, cultivar 4 demonstrated high yield stability across all light conditions, cultivar 10 showed a significant yield advantage under L1 treatment, cultivar 9 performed exceptionally well under L2 treatment, and cultivar 7 had a notable yield advantage under L3 treatment. In 2022, the light environments were further divided into two groups: L1 as one group and L2 and L3 as another (Figure 14B). Cultivars 1 and 2 had higher yields under the L1 condition, indicating good acclimatization to strong light. Cultivars 3 and 11 showed better overall performance across all three light environments, suggesting broad acclimatization. Cultivars 7 and 10 exhibited superior yield performance under L2 and L3 treatments, demonstrating strong shade tolerance. The ranking of genotypes by yield and stability of cultivars in different light environments is shown in Figure 15. In the figure, the smaller the circle surrounding the cultivar, the higher its yield and stability. Based on the comprehensive analysis of data from two years, cultivars 7, 10, and 11 exhibited the most outstanding performance among the 12 tested cultivars, with both high yield and stability.

4. Discussion

4.1. The Change in Leaf Width Is the Basic Feature of Rice Acclimatization to Low Light

Light intensity and shading duration are key environmental drivers of crop plant growth and development, and they ultimately constrain crop yield [16]. They affect yield by influencing the morphological and physiological characteristics of plants [4].
Shading treatments typically have adverse effects on rice growth. In this study, shading significantly reduced tillering number (Figure 2C,D), leaf area index (LAI) (Figure 3E,F), and total dry mass (Figure 5) in rice. Tiller number is a key determinant of rice yield [19], and its reduction directly led to fewer panicles and lower yield. In our research, we found that just 10 days of shading significantly decreased the tiller number, and this effect persisted throughout the entire growing season (Figure 2C,D). The LAI is a reflection of the total leaf area per unit of land area, which is closely related to the light interception ability [20]. The reduction in LAI further resulted in a decline in the canopy photosynthetic capacity of rice. The total dry mass is an important indicator of plant photosynthesis efficiency and biomass [21]. The reduction in total dry mass is a significant factor in yield decline. The low shade tolerance coefficients (STCs) of tiller number, LAI, and total dry mass indicated that shading restricts tiller and canopy expansion, ultimately constraining dry mass accumulation. Among the three inhibited indicators, the STC of tiller number was the lowest, ranging from 38.1% to 62.5%. The greater the deviation of the STC from 100%, the stronger the light plasticity of the trait and the higher its sensitivity to light conditions. The experiment showed that tiller was highly sensitive to light, highlighting the crucial role of light in rice tillering development. In rice cultivation and breeding, we can promote tiller and enhance the shade tolerance and yield stability of rice under low-light conditions through genetic improvement, optimized cultivation techniques, and adjusted planting patterns.
To acclimate to long-term low-light conditions, the rice employed various morphological and physiological regulatory mechanisms to mitigate the adverse effects of shading [22,23,24]. These mechanisms included increases in plant height (Figure 2A,B), leaf chlorophyll content (Figure 4), and leaf-total mass ratio (Figure 6A,B), as well as a decrease in leaf mass per area (LMA) (Figure 3C,D). The increase in plant height enables rice to capture more light in competitive environments [11]. The LMA is an indicator of leaf thickness and density [25]. The decline in LMA indicated that the leaves became thinner, which is an acclimatization strategy of rice to expand the photosynthetic area by limited leaf mass. Chlorophyll, as a key component of the photosynthetic apparatus, plays a vital role in the absorption, transmission, and conversion of light energy [26]. The increase in leaf chlorophyll content enhanced the efficiency of light interception and light energy utilization [27]. Additionally, the increase in leaf-total mass ratio indicated that rice reallocated resources to prioritize the growth of photosynthetic organs [20], thereby maintaining photosynthetic efficiency. Among these acclimatization indicators, the shade tolerance coefficients of the leaf-total mass ratio and leaf chlorophyll content are notably high, reaching 153.2% and 145.8%, respectively (Figure 7). This demonstrated the high light plasticity of leaf chlorophyll content and the leaf-total mass ratio. It means that increasing chlorophyll content and optimizing resource allocation are important mechanisms for rice to acclimate to low-light conditions.
This study observed an intriguing phenomenon: the leaf width of rice exhibited a dynamic response to shading treatments (Figure 3A,B). Short-term shading (10 days) suppressed leaf width expansion; yet by 20 days of shading, leaf width recovered to levels comparable to natural light, and by 40 days, it significantly exceeded natural light controls. These results revealed that rice employed a time-dependent acclimatization strategy to low-light stress, where leaf width was initially constrained by a shade avoidance response [28], but subsequently increased as resource allocation shifted to enhance light capture under prolonged shading, a process potentially mediated by phytohormonal regulation [29]. This progressive alteration in leaf width underscores a sophisticated adaptive mechanism by which rice modulates its phenotype in response to persistent low-light stress.
In summary, shading restricted tiller and canopy expansion, thereby limiting dry mass accumulation. Increasing leaf chlorophyll content and optimizing resource allocation are the key mechanisms for rice to acclimate to low-light conditions. Moreover, the change in leaf width is a significant phenotypic characteristic of rice acclimatizing to low-light environments.

4.2. Synergistic Interactions of Plant Phenotype Drives Low-Light Acclimatization in Rice

Rice acclimates to low-light stress through the synergistic interactions of multiple traits [24]. In this study, the STC of total dry mass was significantly positively correlated with the STC of yield (Figure 9), indicating that enhancing dry mass accumulation was a crucial factor for yield improvement under low-light conditions. Additionally, the STC of total dry mass was positively correlated with those of plant height, tiller number, LAI, and LMA (Figure 10), forming a “plant architecture–photosynthesis synergistic acclimatization” pattern. The coordinated improvement of these traits helps enhance the photosynthetic efficiency and dry mass accumulation capacity of rice under low light. However, the STC of the leaf-total mass ratio was negatively correlated with that of the total dry mass (Figure 10), indicating that rice plants exhibit trade-offs in resource allocation during growth. This trade-off reflects the strategy of enhancing light use efficiency by reallocating more resources to leaves, thereby acclimating to low-light conditions [13]. Although this “leaf-priority” strategy helps maintain growth, it may limit overall biomass accumulation. Therefore, in rice production, it is necessary to balance the relationship between leaf growth and other growth traits to achieve optimal yield. Additionally, the STCs of leaf width and chlorophyll content were positively correlated with that of the leaf-total mass ratio, forming a “leaf morphology–function synergistic acclimatization” module. This indicates that the integration of leaf morphological expansion and enhanced photosynthetic function is an important pathway for expressing light plasticity under low light.
In conclusion, rice acclimatized to low-light conditions through the synergy of dry mass accumulation, plant architecture adjustment, and photosynthetic optimization, though the “leaf priority” strategy may limit overall biomass accumulation.

4.3. Panicle Number Is a Crucial Determinant of Rice Yield Under Low-Light Conditions

Light is a key factor affecting rice yield traits [5]. As shown in our results, shading significantly reduced yield, panicle number, seed-setting rate, and 1000-grain weight (Table 2). Among these inhibited traits, yield and panicle number exhibited relatively low Shading Tolerance Coefficients (STCs) (Figure 8). Furthermore, correlation analysis revealed that the STC of panicle number was most closely associated with that of yield (Figure 11), indicating that the reduction in panicle number is a major factor responsible for yield decline under low-light conditions.
Rice plants exhibit compensatory adjustments in panicle morphology in response to shading [16]. In this study, under 50% shading treatment, the number of primary branches, number of secondary branches, and grain number per panicle all increased (Table 2), with STCs exceeding 100% (Figure 8). This suggests that rice can partially mitigate yield loss by optimizing panicle structure under moderate low-light conditions. However, under 75% shading conditions, these traits were significantly suppressed, indicating that severe light reduction exceeds the regulatory capacity of rice phenotypic plasticity, leading to a breakdown of compensatory mechanisms.
These findings imply that effective strategies could have been adopted to enhance rice acclimatization to low-light environments. In practical production, appropriate measures such as optimizing planting density, implementing precise fertilization, and improving irrigation management can promote tillering and increase panicle number under shading conditions. Meanwhile, in shade-tolerant breeding programs, priority should be given to selecting genotypes with high panicle number under normal light conditions and the ability to maintain panicle formation under low light. Moreover, attention should also be paid to the plasticity of panicle structure traits, which serves as an important compensatory mechanism under moderate shading. Future research should quantify the genotypic variation in panicle plasticity and incorporate this trait into high-yield breeding programs for low-light environments.

4.4. Comprehensive Evaluation of Rice Shade Tolerance

The yield shade tolerance coefficient (STC) is a key indicator to evaluate the shade tolerance of rice cultivars. According to the combined data from the two-year trials, the majority of tested rice cultivars exhibited good shade tolerance under 50% shading conditions (Figure 12), and under 75% shading conditions, cultivars Jia You 968 (5), Zhong You 596 (6), Le You 918 (7), Q You 12 (8), and Shen 9 You 28 (11) exhibited relatively higher yield STCs across the two years (Figure 12), suggesting these cultivars may possess better shade tolerance under severe shading. Notably, the STC for grain yield under severe shade in 2022 was markedly lower than that in 2021. This decline likely reflects the warmer, drier, and higher-radiation conditions recorded during the growing season in 2022 (Figure 1, Table A1), which increased the differences in yield between the 75% shade and natural light treatments, thereby intensifying genotype and environment interactions. These results highlight the need to consider over year variations in temperature and solar radiation in assessing rice genotype stability and shade tolerance for breeding. Further analysis of actual field yields showed that cultivars Gang You 952 (3), Le You 918 (7), Chuankang You 65 (10), and Shen 9 You 28 (11) had more pronounced yield performance under moderate shading (Figure 13). Notably, cultivars Le You 918 (7) and Shen 9 You 28 (11) also demonstrated high yield under severe shading, suggesting that genotypes performing well under moderate shade may serve as genetic sources for tolerance to more intense shading.
The GGE biplot proved to be an effective tool for both identifying cultivars with superior performance under specific light conditions and assessing their broad acclimatization [30]. Comprehensive analysis over two years revealed that cultivars Le You 918 (7), Chuankang You 65 (10), and Shen 9 You 28 (11) exhibited outstanding yield and stability across different light conditions (Figure 14 and Figure 15), indicating their strong environmental acclimatization and strong shade tolerance.
Taking into account the results from the GGE biplot, the yield STC, and yield, cultivars Le You 918 (7) and Shen 9 You 28 (11) were the best materials combining optimal shade tolerance with yield stability in this experiment.

5. Conclusions

Rice acclimates to low-light conditions through the synergistic interactions of various traits, with increasing leaf chlorophyll content, optimizing resource allocation, and adjusting leaf width being key acclimatization mechanisms. Promoting tillering and canopy expansion, increasing panicle number, and optimizing panicle structure-related traits are crucial pathways for enhancing rice yield under low-light conditions. In this study, Le You 918 and Shen 9 You 28 showed the potential for high yield and strong shade tolerance. In future studies, including multi-location and multi-year trials, more attention should be paid to exploring the stability of shade tolerance formation mechanisms to breed shade-tolerant rice.

Author Contributions

Conceptualization, S.Y. and L.L.; methodology, H.Y.; software, S.G.; validation, Y.L. (Yan Liu) and Y.K.; formal analysis, X.L.; investigation, G.W., Y.L. (Yufei Liu) and Z.L.; resources, H.Y.; data curation, S.Y.; writing—original draft preparation, S.Y.; writing—review and editing, L.L., G.W., Y.L. (Yan Liu), Y.K., Y.L. (Yufei Liu), Z.L. and S.G.; visualization, S.Y.; supervision, H.Y.; project administration, H.Y.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the National Natural Science Foundation of China (31901451), the Special Key Project of Technology Innovation and Application of Chongqing (CSTB2022TIAD-KPX0015), and the Fundamental Research Funds for the Central Universities (SWU-KT25030).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
LMALeaf mass per area
LAILeaf area index
ChlChlorophyll
CarCarotenoid
STCShade tolerance coefficient

Appendix A

Agronomy 15 02855 g0a1
Figure A1. Differences in Car under different light conditions. (A) Car in 2021; (B) Car in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Figure A1. Differences in Car under different light conditions. (A) Car in 2021; (B) Car in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Agronomy 15 02855 g0a1
Table A1. Monthly cumulative sunshine duration and total solar radiation at the experimental site in 2021 and 2022.
Table A1. Monthly cumulative sunshine duration and total solar radiation at the experimental site in 2021 and 2022.
MonthSunshine Duration (h)Total Solar Radiation (MJ m−2)
2021202220212022
Apr117.7224.5133.7202.6
May187.1213.0182.9209.0
Jun241.1228.2197.4202.3
Jul274.1342.1250.5304.6
Aug237.5345.4214.8310.4
Sep243.4167.5220.8165.5
Table A2. Rice yield (kg hm−2) under different lighting conditions.
Table A2. Rice yield (kg hm−2) under different lighting conditions.
Cultivar NO.20212022
L1L2L3L1L2L3
17973 ± 1422 a4037 ± 568 b1904 ± 332 c9776 ± 1125 a4868 ± 1139 b2863 ± 605 c
27662 ± 221 a4077 ± 334 b2267 ± 757 c9462 ± 1033 a5157 ± 385 b2611 ± 427 c
37184 ± 719 a5725 ± 516 b2656 ± 758 c9435 ± 1267 a7601 ± 473 b3268 ± 734 c
48681 ± 608 a6642 ± 662 b5167 ± 291 c8271 ± 494 a5353 ± 945 b3023 ± 213 c
57608 ± 601 a4515 ± 449 b4619 ± 475 b7192 ± 454 a5434 ± 297 b2888 ± 525 c
66346 ± 806 a4528 ± 976 b2720 ± 517 c7518 ± 470 a5931 ± 788 b4040 ± 415 c
77396 ± 490 a5770 ± 819 b4662 ± 792 b7914 ± 1069 a6494 ± 840 b3560 ± 331 c
86736 ± 218 a4961 ± 776 b3507 ± 630 c7257 ± 292 a5652 ± 546 b3025 ± 590 c
98016 ± 1347 a6336 ± 987 b3892 ± 795 c7605 ± 832 a5180 ± 776 b2839 ± 585 c
108380 ± 246 a6118 ± 1063 b3329 ± 855 c8179 ± 811 a6727 ± 953 b3306 ± 700 c
117489 ± 568 a4846 ± 871 b4903 ± 149 b9654 ± 701 a6438 ± 698 b4347 ± 785 c
127285 ± 1080 a5272 ± 761 b2512 ± 537 c7034 ± 939 a4816 ± 615 b2869 ± 388 c
Note: L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments. Cultivar codes: 1 (Zhong 9 You 804), 2 (Xida 8 You 727), 3 (Gang You 952), 4 (Rong You 184), 5 (Jia You 968), 6 (Zhong You 596), 7 (Le You 918), 8 (Q You 12), 9 (Yuxiang 203), 10 (Chuankang You 65), 11 (Shen 9 You 28), and 12 (II You 602).
Table A3. Three-way ANOVA of rice yield traits for light (L), cultivar (C), and year (Y) effects and their interactions.
Table A3. Three-way ANOVA of rice yield traits for light (L), cultivar (C), and year (Y) effects and their interactions.
FactorsPanicle NumberPanicle Length (cm)Number of Primary BranchesNumber of Secondary BranchesGrain Number per PanicleSeed-Setting Rate (%)1000-Grain Weight (g)Grain Yield (kg hm−2)
Light***********************
Cultivar************************
Year************************
Light × Cultivar************************
Light × Yearns***ns******ns******
Cultivar × Year************************
Light × Cultivar × Year******************
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, ns = no significant (F-test).
Table A4. Three-way ANOVA of rice agronomic traits for light (L), cultivar (C), and year (Y) effects and their interactions (shading for 10 days).
Table A4. Three-way ANOVA of rice agronomic traits for light (L), cultivar (C), and year (Y) effects and their interactions (shading for 10 days).
FactorsPlant Height (cm)Tiller NumberTotal Dry Mass (g)Leaf-Total Mass RatioRoot-Shoot RatioLAILeaf Width (mm)Leaf Mass per Area
(g m−2)		Chl(a + b) (mg m−2)
Light*************************
Cultivar*********ns***************
Year***ns*******************
Light × Cultivar*********ns*****ns****
Light × Year**************************
Cultivar × Year*********ns************ns
Light × Cultivar × Year**********ns***ns**ns
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, ns = no significant (F-test).
Table A5. Three-way ANOVA of rice agronomic traits for light (L), cultivar (C), and year (Y) effects and their interactions (shading for 20 days).
Table A5. Three-way ANOVA of rice agronomic traits for light (L), cultivar (C), and year (Y) effects and their interactions (shading for 20 days).
FactorsPlant Height (cm)Tiller NumberTotal Dry Mass (g)Leaf-Total Mass RatioRoot-Shoot RatioLAILeaf Width (mm)Leaf Mass per Area
(g m−2)		Chl(a + b) (mg m−2)
Light***************************
Cultivar***************************
Year*****ns*********ns***ns
Light × Cultivarns*********ns***ns***ns
Light × Yearns*********ns***********
Cultivar × Year**********ns*********ns
Light × Cultivar × Year********************ns
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, ns = no significant (F-test).
Table A6. Three-way ANOVA of rice agronomic traits for light (L), cultivar (C), and year (Y) effects and their interactions (shading for 40 days).
Table A6. Three-way ANOVA of rice agronomic traits for light (L), cultivar (C), and year (Y) effects and their interactions (shading for 40 days).
FactorsPlant Height (cm)Tiller NumberTotal Dry Mass (g)Leaf-Total Mass RatioRoot-Shoot RatioLAILeaf Width (mm)Leaf Mass per Area
(g m−2)		Chl(a + b) (mg m−2)
Light***************************
Cultivar***************************
Year****************nsns***
Light × Cultivar**************************
Light × Year**********ns******ns***
Cultivar × Yearns*********ns****ns**
Light × Cultivar × Year******************ns***
Note: * p < 0.05, ** p < 0.01, *** p < 0.001, ns = no significant (F-test).

References

  1. Hatzianastassiou, N.; Papadimas, C.D.; Matsoukas, C.; Pavlakis, K.; Fotiadi, A.; Wild, M.; Vardavas, I. Recent regional surface solar radiation dimming and brightening patterns: Inter-hemispherical asymmetry and a dimming in the Southern Hemisphere. Atmos. Sci. Lett. 2012, 13, 43–48. [Google Scholar] [CrossRef]
  2. Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.; Matsoukas, C.; Wild, M.; Vardavas, I. Which are the main drivers of global dimming and brightening? Atmos. Res. 2025, 322, 108140. [Google Scholar] [CrossRef]
  3. Liu, T.T.; Han, Y.; Jiang, W.H. Distribution Characters of Fog in Chongqing and Causes of Fog in November 7, 2006. Adv. Mater. Res. 2013, 726–731, 1297–1300. [Google Scholar] [CrossRef]
  4. Ma, P.; Zhou, L.; Liao, X.; Zhang, K.; Aer, L.; Yang, E.; Deng, J.; Zhang, R. Effects of low light after heading on the yield of direct seeding rice and its physiological response mechanism. Plants 2023, 12, 4077. [Google Scholar] [CrossRef] [PubMed]
  5. Chen, H.; Li, Q.; Zeng, Y.; Deng, F.; Ren, W. Effect of different shading materials on grain yield and quality of rice. Sci. Rep. 2019, 9, 9992. [Google Scholar] [CrossRef] [PubMed]
  6. Shafiq, I.; Hussain, S.; Raza, M.; Iqbal, N.; Ahsan Asghar, M.; Raza, A.; Fan, Y.; Mumtaz, M.; Shoaib, M.; Ansar, M.; et al. Crop photosynthetic response to light quality and light intensity. J. Integr. Agric. 2021, 20, 4–23. [Google Scholar] [CrossRef]
  7. Resurreccion, A.; Makino, A.; Bennett, J.; Mae, T. Effect of light intensity on the growth and photosynthesis of rice under different sulfur concentrations. Soil Sci. Plant Nutr. 2002, 48, 71–77. [Google Scholar] [CrossRef]
  8. Hu, Y.; Li, F.; Yu, E.; Sun, L.; Guo, J.; Xing, Z.; Guo, B.; Wei, H.; Huo, Z.; Xu, K.; et al. Effect of low temperature and weak light combined stress during panicle differentiation on grain yield and physiological property in rice. J. Agron. Crop Sci. 2025, 211, e70069. [Google Scholar] [CrossRef]
  9. Zhou, H.; Chen, X.; Li, M.; Shi, C.; Jiang, M. Simulation model for assessing high-temperature stress on rice. Agronomy 2024, 14, 900. [Google Scholar] [CrossRef]
  10. Li, Q.; Deng, F.; Zeng, Y.; Li, B.; He, C.; Zhu, Y.; Zhou, X.; Zhang, Z.; Wang, L.; Tao, Y.; et al. Low light stress increases chalkiness by disturbing starch synthesis and grain filling of rice. Int. J. Mol. Sci. 2022, 23, 9153. [Google Scholar] [CrossRef]
  11. Du, Y.; Ji, X.; Zhang, J.; Li, J.; Sun, H.; Zhao, Q. Research progress on the impacts of low light intensity on rice growth and development. Chin. J. Eco-Agric. 2013, 21, 1307–1317. [Google Scholar] [CrossRef]
  12. Ganguly, S.; Nimitha, K.; Saha, S.; Mahapatra, N.; Bhattacharya, K.; Kundu, R.; Ganguly, S.; Sen, P.; Saha, A.; Purkayastha, S.; et al. Identification and analysis of low light responsive yield enhancing QTLs in rice. Sci. Rep. 2024, 14, 21011. [Google Scholar] [CrossRef]
  13. Lafarge, T.; Seassau, C.; Martin, M.; Bueno, C.; Clément-Vidal, A.; Schreck, E.; Luquet, D. Regulation and recovery of sink strength in rice plants grown under changes in light intensity. Funct. Plant Biol. 2010, 37, 413–428. [Google Scholar] [CrossRef]
  14. Kumar, U.; Laza, M.; Soulié, J.; Pasco, R.; Mendez, K.; Dingkuhn, M. Compensatory phenotypic plasticity in irrigated rice: Sequential formation of yield components and simulation with SAMARA model. Field Crops Res. 2016, 193, 164–177. [Google Scholar] [CrossRef]
  15. Saha, S.; Purkayastha, S.; Nimitha, K.; Ganguly, S.; Das, S.; Ganguly, S.; Mahapatra, N.; Bhattacharya, K.; Das, D.; Saha, A.; et al. Rice (Oryza sativa) alleviates photosynthesis and yield loss by limiting specific leaf weight under low light intensity. Funct. Plant Biol. 2023, 50, 267–276. [Google Scholar] [CrossRef] [PubMed]
  16. Wang, L.; Deng, F.; Ren, W. Shading tolerance in rice is related to better light harvesting and use efficiency and grain filling rate during grain filling period. Field Crops Res. 2015, 180, 54–62. [Google Scholar] [CrossRef]
  17. Cheng, Y.; Xiao, F.; Huang, D.; Yang, Y.; Cheng, W.; Jin, S.; Li, G.; Ding, Y.; Paul, M.; Liu, Z. High canopy photosynthesis before anthesis explains the outstanding yield performance of rice cultivars with ideal plant architecture. Field Crops Res. 2024, 306, 109223. [Google Scholar] [CrossRef]
  18. Lichtenthaler, H.K.; Wellburn, A.R. Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Analysis 1983, 11, 591–592. [Google Scholar] [CrossRef]
  19. Wang, X.; Zou, D.; Li, C.; Zhou, W.; Li, K.; Tang, Q.; Zhu, X.; Li, X.; Cao, L. Analysis of characteristics of rice tillering dynamics influenced by sowing dates based on DTM. Heliyon 2024, 10, e38443. [Google Scholar] [CrossRef]
  20. Deng, J.; Sheng, T.; Zhong, X.; Ye, J.; Wang, C.; Huang, L.; Tian, X.; Liu, K.; Zhang, Y. Delayed leaf senescence improves radiation use efficiency and explains yield advantage of large panicle-type hybrid rice. Plants 2023, 12, 4063. [Google Scholar] [CrossRef]
  21. Honda, S.; Ohkubo, S.; San, N.; Nakkasame, A.; Tomisawa, K.; Katsura, K.; Ookawa, T.; Nagano, A.; Adachi, S. Maintaining higher leaf photosynthesis after heading stage could promote biomass accumulation in rice. Sci. Rep. 2021, 11, 7579. [Google Scholar] [CrossRef]
  22. Duan, B.; Lü, Y.; Yin, C.; Li, C. Morphological and physiological plasticity of woody plant in response to high light and low light. Chin. J. Appl. Environ. Biol. 2005, 11, 238–245. [Google Scholar] [CrossRef]
  23. Loretta, G. Plant Phenotypic Plasticity in Response to Environmental Factors. Adv. Bot. 2014, 2014, 208747. [Google Scholar] [CrossRef]
  24. Ghorbel, M.; Brini, F.; Brestic, M.; Landi, M. Interplay between low light and hormone-mediated signaling pathways in shade avoidance regulation in plants. Plant Stress 2023, 9, 100178. [Google Scholar] [CrossRef]
  25. Sancho-Knapik, D.; Escudero, A.; Mediavilla, S.; Scoffoni, C.; Zailaa, J.; Cavender-Bares, J.; Alvarez-Arenas, T.; Molins, A.; Alonso-Forn, D.; Ferrio, J.; et al. Deciduous and evergreen oaks show contrasting adaptive responses in leaf mass per area across environments. New Phytol. 2021, 230, 521–534. [Google Scholar] [CrossRef] [PubMed]
  26. Baker, N. Chlorophyll fluorescence: A probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 2008, 59, 89–113. [Google Scholar] [CrossRef] [PubMed]
  27. Li, H.; Jiang, D.; Wollenweber, B.; Dai, T.; Cao, W. Effects of shading on morphology, physiology and grain yield of winter wheat. Eur. J. Agron. 2010, 33, 267–275. [Google Scholar] [CrossRef]
  28. Pierik, R.; Ballaré, C. Control of plant growth and defense by photoreceptors: From mechanisms to opportunities in agriculture. Mol. Plant 2021, 14, 61–76. [Google Scholar] [CrossRef]
  29. Gommers, C.M.M.; Visser, E.J.W.; Onge, K.R.S.; Voesenek, L.A.C.J.; Pierik, R. Shade tolerance: When growing tall is not an option. Trends Plant Sci. 2013, 18, 65–71. [Google Scholar] [CrossRef]
  30. Saeidnia, F.; Taherian, M.; Nazeri, S. Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates. BMC Plant Biol. 2023, 23, 198. [Google Scholar] [CrossRef] [PubMed]
Agronomy 15 02855 g001
Figure 1. Meteorological data on daily maximum and minimum temperatures and precipitation at the experimental site in 2021 and 2022. (A) Meteorological data in 2021; (B) meteorological data in 2022.
Figure 1. Meteorological data on daily maximum and minimum temperatures and precipitation at the experimental site in 2021 and 2022. (A) Meteorological data in 2021; (B) meteorological data in 2022.
Agronomy 15 02855 g001
Agronomy 15 02855 g002
Figure 2. Differences in plant height and tiller number of rice under different light conditions. (A) Plant height in 2021; (B) plant height in 2022; (C) tiller number in 2021; and (D) tiller number in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Figure 2. Differences in plant height and tiller number of rice under different light conditions. (A) Plant height in 2021; (B) plant height in 2022; (C) tiller number in 2021; and (D) tiller number in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Agronomy 15 02855 g002
Agronomy 15 02855 g003
Figure 3. Differences in leaf width, LMA, and LAI under different light conditions. LMA, Leaf mass per area; LAI, leaf area index. (A) Leaf width in 2021; (B) leaf width in 2022; (C) LMA in 2021; (D) LMA in 2022; (E) LAI in 2021; and (F) LAI in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Figure 3. Differences in leaf width, LMA, and LAI under different light conditions. LMA, Leaf mass per area; LAI, leaf area index. (A) Leaf width in 2021; (B) leaf width in 2022; (C) LMA in 2021; (D) LMA in 2022; (E) LAI in 2021; and (F) LAI in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Agronomy 15 02855 g003
Agronomy 15 02855 g004
Figure 4. Differences in Chl(a + b) under different light conditions. (A) Chl(a + b) in 2021; (B) Chl(a + b) in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Figure 4. Differences in Chl(a + b) under different light conditions. (A) Chl(a + b) in 2021; (B) Chl(a + b) in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Agronomy 15 02855 g004
Agronomy 15 02855 g005
Figure 5. Differences in the total dry mass of rice under different light conditions. (A) Total dry mass in 2021; (B) total dry mass in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Figure 5. Differences in the total dry mass of rice under different light conditions. (A) Total dry mass in 2021; (B) total dry mass in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Agronomy 15 02855 g005
Agronomy 15 02855 g006
Figure 6. Differences in the leaf-total mass ratio and root-shoot ratio of rice under different light conditions. (A) Leaf-total mass ratio in 2021; (B) leaf-total mass ratio in 2022; (C) root-shoot ratio in 2021; and (D) root-shoot ratio in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Figure 6. Differences in the leaf-total mass ratio and root-shoot ratio of rice under different light conditions. (A) Leaf-total mass ratio in 2021; (B) leaf-total mass ratio in 2022; (C) root-shoot ratio in 2021; and (D) root-shoot ratio in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments at the same time period. Each point within the box represents the mean of the parameters in each cultivar (n = 3 subplots) under different light treatments.
Agronomy 15 02855 g006
Agronomy 15 02855 g007
Figure 7. Shade tolerance coefficient (STC) of rice morphological characteristics and physiological properties. (A) STC after shading for 10 days in 2021; (B) STC after shading for 10 days in 2022; (C) STC after shading for 20 days in 2021; (D) STC after shading for 20 days in 2022; (E) STC after shading for 40 days in 2021; and (F) STC after shading for 40 days in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Values are means across all twelve cultivars (n = 36 subplots = 12 cultivars × 3 replicates).
Figure 7. Shade tolerance coefficient (STC) of rice morphological characteristics and physiological properties. (A) STC after shading for 10 days in 2021; (B) STC after shading for 10 days in 2022; (C) STC after shading for 20 days in 2021; (D) STC after shading for 20 days in 2022; (E) STC after shading for 40 days in 2021; and (F) STC after shading for 40 days in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Values are means across all twelve cultivars (n = 36 subplots = 12 cultivars × 3 replicates).
Agronomy 15 02855 g007
Agronomy 15 02855 g008
Figure 8. Shade tolerance coefficient of rice yield traits. (A) Shade tolerance coefficient in 2021; (B) shade tolerance coefficient in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Values are means across all twelve cultivars (n = 36 subplots = 12 cultivars × 3 replicates).
Figure 8. Shade tolerance coefficient of rice yield traits. (A) Shade tolerance coefficient in 2021; (B) shade tolerance coefficient in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Values are means across all twelve cultivars (n = 36 subplots = 12 cultivars × 3 replicates).
Agronomy 15 02855 g008
Agronomy 15 02855 g009
Figure 9. The relationship between total dry mass STC and yield STC. Data are STC means of each cultivar under L2 and L3 treatments across the three sampling dates.
Figure 9. The relationship between total dry mass STC and yield STC. Data are STC means of each cultivar under L2 and L3 treatments across the three sampling dates.
Agronomy 15 02855 g009
Agronomy 15 02855 g010
Figure 10. Correlation of shade tolerance coefficients between morphological and physiological Traits. Data are STC means of each cultivar under L2 and L3 treatments across the three sampling dates.
Figure 10. Correlation of shade tolerance coefficients between morphological and physiological Traits. Data are STC means of each cultivar under L2 and L3 treatments across the three sampling dates.
Agronomy 15 02855 g010
Agronomy 15 02855 g011
Figure 11. Correlation of shade tolerance coefficients of yield traits. Data are STC means of each cultivar under L2 and L3 treatments across the three sampling dates.
Figure 11. Correlation of shade tolerance coefficients of yield traits. Data are STC means of each cultivar under L2 and L3 treatments across the three sampling dates.
Agronomy 15 02855 g011
Agronomy 15 02855 g012
Figure 12. Differences in yield shade tolerance coefficient among rice cultivars. (A) Yield shade tolerance coefficient in 2021; (B) yield shade tolerance coefficient in 2022. L2 and L3 represent 50% shading and 75% shading, respectively. Cultivar codes: 1 (Zhong 9 you 804), 2 (Xida 8 you 727), 3 (Gang you 952), 4 (Rong you 184), 5 (Jia you 968), 6 (Zhong you 596), 7 (Le you 918), 8 (Q you 12), 9 (Yuxiang 203), 10 (Chuankang you 65), 11 (Shen 9 you 28), and 12 (II you 602).
Figure 12. Differences in yield shade tolerance coefficient among rice cultivars. (A) Yield shade tolerance coefficient in 2021; (B) yield shade tolerance coefficient in 2022. L2 and L3 represent 50% shading and 75% shading, respectively. Cultivar codes: 1 (Zhong 9 you 804), 2 (Xida 8 you 727), 3 (Gang you 952), 4 (Rong you 184), 5 (Jia you 968), 6 (Zhong you 596), 7 (Le you 918), 8 (Q you 12), 9 (Yuxiang 203), 10 (Chuankang you 65), 11 (Shen 9 you 28), and 12 (II you 602).
Agronomy 15 02855 g012
Agronomy 15 02855 g013
Figure 13. Differences in yield among rice cultivars. (A) Yield in 2021; (B) yield in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments. Cultivar codes: 1 (Zhong 9 you 804), 2 (Xida 8 you 727), 3 (Gang you 952), 4 (Rong you 184), 5 (Jia you 968), 6 (Zhong you 596), 7 (Le you 918), 8 (Q you 12), 9 (Yuxiang 203), 10 (Chuankang you 65), 11 (Shen 9 you 28), and 12 (II you 602).
Figure 13. Differences in yield among rice cultivars. (A) Yield in 2021; (B) yield in 2022. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Different lowercase letters indicate significant differences among different light treatments. Cultivar codes: 1 (Zhong 9 you 804), 2 (Xida 8 you 727), 3 (Gang you 952), 4 (Rong you 184), 5 (Jia you 968), 6 (Zhong you 596), 7 (Le you 918), 8 (Q you 12), 9 (Yuxiang 203), 10 (Chuankang you 65), 11 (Shen 9 you 28), and 12 (II you 602).
Agronomy 15 02855 g013
Agronomy 15 02855 g014
Figure 14. Acclimatization of cultivars in different light environments based on a GGE biplot. (A) Acclimatization of cultivars in 2021; (B) acclimatization of cultivars in 2022. The biplot is based on environment-centered (Centering = 2) and scaled data (Scaling = 1), using a genotype-focused singular value partitioning (SVP = 1) method. PC1 and PC2 collectively explain 84.9% (2021) and 85.9% (2022) of the total G + GE variance, supporting the reliability of the results. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Cultivar codes: 1 (Zhong 9 You 804), 2 (Xida 8 You 727), 3 (Gang You 952), 4 (Rong You 184), 5 (Jia You 968), 6 (Zhong You 596), 7 (Le You 918), 8 (Q You 12), 9 (Yuxiang 203), 10 (Chuankang You 65), 11 (Shen 9 You 28), and 12 (II You 602).
Figure 14. Acclimatization of cultivars in different light environments based on a GGE biplot. (A) Acclimatization of cultivars in 2021; (B) acclimatization of cultivars in 2022. The biplot is based on environment-centered (Centering = 2) and scaled data (Scaling = 1), using a genotype-focused singular value partitioning (SVP = 1) method. PC1 and PC2 collectively explain 84.9% (2021) and 85.9% (2022) of the total G + GE variance, supporting the reliability of the results. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Cultivar codes: 1 (Zhong 9 You 804), 2 (Xida 8 You 727), 3 (Gang You 952), 4 (Rong You 184), 5 (Jia You 968), 6 (Zhong You 596), 7 (Le You 918), 8 (Q You 12), 9 (Yuxiang 203), 10 (Chuankang You 65), 11 (Shen 9 You 28), and 12 (II You 602).
Agronomy 15 02855 g014
Agronomy 15 02855 g015
Figure 15. Ranking of genotypes by yield and stability of cultivars in different light environments based on a GGE biplot. (A) Ranking of genotypes in 2021; (B) ranking of genotypes in 2022. The biplot is based on environment-centered (Centering = 2) and scaled data (Scaling = 1), using a genotype-focused singular value partitioning (SVP = 1) method. PC1 and PC2 collectively explain 84.9% (2021) and 85.9% (2022) of the total G + GE variance, supporting the reliability of the results. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Cultivar codes: 1 (Zhong 9 You 804), 2 (Xida 8 You 727), 3 (Gang You 952), 4 (Rong You 184), 5 (Jia You 968), 6 (Zhong You 596), 7 (Le You 918), 8 (Q You 12), 9 (Yuxiang 203), 10 (Chuankang You 65), 11 (Shen 9 You 28), and 12 (II You 602).
Figure 15. Ranking of genotypes by yield and stability of cultivars in different light environments based on a GGE biplot. (A) Ranking of genotypes in 2021; (B) ranking of genotypes in 2022. The biplot is based on environment-centered (Centering = 2) and scaled data (Scaling = 1), using a genotype-focused singular value partitioning (SVP = 1) method. PC1 and PC2 collectively explain 84.9% (2021) and 85.9% (2022) of the total G + GE variance, supporting the reliability of the results. L1, L2, and L3 represent natural light, 50% shading, and 75% shading, respectively. Cultivar codes: 1 (Zhong 9 You 804), 2 (Xida 8 You 727), 3 (Gang You 952), 4 (Rong You 184), 5 (Jia You 968), 6 (Zhong You 596), 7 (Le You 918), 8 (Q You 12), 9 (Yuxiang 203), 10 (Chuankang You 65), 11 (Shen 9 You 28), and 12 (II You 602).
Agronomy 15 02855 g015
Table 1. The rice cultivars used in the experiment.
Table 1. The rice cultivars used in the experiment.
NO.CultivarTypeNO.CultivarType
1Zhong 9 You 804indica7Le You 918indica
2Xida 8 You 727indica8Q You 12indica
3Gang You 952indica9Yuxiang 203indica
4Rong You 184indica10Chuankang You 65indica
5Jia You 968indica11Shen 9 You 28indica
6Zhong You 596indica12II You 602indica
Table 2. Effect of different light treatments on rice yield traits.
Table 2. Effect of different light treatments on rice yield traits.
YearParameterL1L2L3
2021Panicle number6.38 ± 1.23 a4.63 ± 0.95 b3.55 ± 0.97 c
Panicle length (cm)26.48 ± 1.91 b27.07 ± 2.98 b28.50 ± 2.81 a
Number of primary branches12.94 ± 1.85 b14.49 ± 2.41 a12.59 ± 1.63 b
Number of secondary branches38.49 ± 7.23 a40.85 ± 11.16 a37.23 ± 12.91 a
Grain number per panicle225.29 ± 38.86 a231.63 ± 46.72 a220.44 ± 52.38 a
Seed-setting rate (%)89.67 ± 5.31 a85.49 ± 8.40 b75.82 ± 12.66 c
1000-grain weight (g)27.32 ± 4.01 a26.28 ± 2.58 a26.19 ± 2.79 a
Grain yield (kg hm−2)7631.84 ± 985.71 a5268.16 ± 1103.77 b3414.09 ± 1198.35 c
2022Panicle number6.96 ± 1.27 a4.91 ± 0.96 b4.10 ± 0.64 c
Panicle length (cm)29.15 ± 1.95 ab30.03 ± 2.71 a28.29 ± 3.32 b
Number of primary branches14.00 ± 1.14 b15.48 ± 1.42 a13.86 ± 1.38 b
Number of secondary branches48.73 ± 8.62 b58.25 ± 12.17 a42.65 ± 11.82 c
Grain number per panicle255.19 ± 42.63 b312.77 ± 62.90 a242.38 ± 52.74 b
Seed-setting rate (%)82.41 ± 6.26 a77.43 ± 8.49 b68.55 ± 11.50 c
1000-grain weight (g)25.68 ± 2.24 a22.51 ± 2.27 b21.71 ± 1.96 c
Grain yield (kg hm−2)8252.50 ± 1242.88 a5827.01 ± 1080.51 b3229.00 ± 709.41 c
Note: Values are means ± SD. Values are means across all twelve cultivars (n = 36 subplots = 12 cultivars × 3 replicates). Different lowercase letters indicate significant differences among different light treatments.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, S.; Li, L.; Wang, G.; Liu, Y.; Kong, Y.; Li, X.; Liu, Y.; Lei, Z.; Gul, S.; He, G.; et al. Rice Responds to Different Light Conditions by Adjusting Leaf Phenotypic and Panicle Traits to Optimize Shade Tolerance Stability and Yield. Agronomy 2025, 15, 2855. https://doi.org/10.3390/agronomy15122855

AMA Style

Yang S, Li L, Wang G, Liu Y, Kong Y, Li X, Liu Y, Lei Z, Gul S, He G, et al. Rice Responds to Different Light Conditions by Adjusting Leaf Phenotypic and Panicle Traits to Optimize Shade Tolerance Stability and Yield. Agronomy. 2025; 15(12):2855. https://doi.org/10.3390/agronomy15122855

Chicago/Turabian Style

Yang, Shihui, Lingyi Li, Guangyuan Wang, Yan Liu, Ying Kong, Xianghui Li, Yufei Liu, Zhensheng Lei, Shareef Gul, Guanghua He, and et al. 2025. "Rice Responds to Different Light Conditions by Adjusting Leaf Phenotypic and Panicle Traits to Optimize Shade Tolerance Stability and Yield" Agronomy 15, no. 12: 2855. https://doi.org/10.3390/agronomy15122855

APA Style

Yang, S., Li, L., Wang, G., Liu, Y., Kong, Y., Li, X., Liu, Y., Lei, Z., Gul, S., He, G., & Yao, H. (2025). Rice Responds to Different Light Conditions by Adjusting Leaf Phenotypic and Panicle Traits to Optimize Shade Tolerance Stability and Yield. Agronomy, 15(12), 2855. https://doi.org/10.3390/agronomy15122855

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop