Photosynthetic Plasticity and Stomata Adjustment in Chromosome Segment Substitution Lines of Rice Cultivar KDML105 under Drought Stress

The impact of increasing drought periods on crop yields as a result of global climate change is a major concern in modern agriculture. Thus, a greater understanding of crop physiological responses under drought stress can guide breeders to develop new cultivars with enhanced drought tolerance. In this study, selected chromosome segment substitution lines of KDML105 (KDML105-CSSL) were grown in the Plant Phenomics Center of Kasetsart University in Thailand under well-watered and drought-stressed conditions. Physiological traits were measured by observing gas exchange dynamics and using a high-throughput phenotyping platform. Furthermore, because of its impact on plant internal gas and water regulation, stomatal morphological trait variation was recorded. The results show that KDML105-CSS lines exhibited plasticity responses to enhance water-use efficiency which increased by 3.62%. Moreover, photosynthesis, stomatal conductance and transpiration decreased by approximately 40% and plant height was reduced by 17.69%. Stomatal density tended to decrease and was negatively correlated with stomatal size, and stomata on different sides of the leaves responded differently under drought stress. Under drought stress, top-performing KDML105-CSS lines with high net photosynthesis had shorter plant height and improved IWUE, as influenced by an increase in stomatal density on the upper leaf side and a decrease on the lower leaf side.


Introduction
Perhaps the most significant issue facing modern agriculture is climate change. Climate change negatively affects crops due to changes brought about by temperature and precipitation [1]. By 2050, the predicted worldwide population will be approximately ten billion people. As a result, doubled global crop production will be required to meet rising food demand. Under predicted climate change scenarios, approximately 50% of people will face water shortages for consumption, with subsequent water deficits in agriculture [2][3][4]. The incidence and magnitude of drought has increased due to climate change which has limited crop production in both quantity and quality [5,6]. Drought is a major issue in Thailand, impeding rice production, which is an important cash crop for the country [7]. Drought stress can affect any stage of rice growth and may thus cause reductions in yield [8]. Although there are irrigation systems for rice cultivation, around 50% of the Thai rice crop is rainfed. Rainfed cultivation is significantly needed as a natural environment and an important ecosystem for rice production [9]. According to research, precipitation in Thailand Table 1. Statistical summary and the effects of genotype (G effect), treatment (TRT effect) and plasticity on physiological and stomatal traits; well-watered (WW), drought stress (DS). The significance level is indicated as '*' depending on p-values (p < 0.05 '*', p < 0.01 '**'). For all physiological traits, we found highly significant differences among treatments. Overall, variation within each condition, including plasticity, was not found. In drought stress conditions, however, there was significant variation among lines in stomatal conduc- tance (g s ) and transpiration rate (E) (p < 0.05). Under irrigated conditions, KDML105-CSS lines (including their donors) had higher rates of net photosynthesis (P n ), stomatal conductance, and transpiration rate than under drought stress. The three mentioned traits were reduced under drought stress by approximately 38% (Tables 1 and 2).

Traits
The intrinsic water-use efficiency (IWUE) differed considerably from the previous traits. There was a 3.62% increase in the mean IWUE under drought stress (Table 1). Another trait describing photosynthesis, maximum PSII quantum yield (QYmax), displayed the same pattern as net photosynthesis rate. However, average QYmax decreased by 10% under drought stress, which was less than the 38% decrease in net photosynthesis rate (Tables 1 and 2).
Genetic variation was significantly associated with plant height under drought stress. Plant height averaged 695.33 mm in well-watered conditions and 568.34 mm in droughtstressed conditions, respectively. A decrease of 17.7% was observed for plant height under drought conditions (Tables 1 and 2).
Stomata morphological traits were collected on both abaxial and adaxial leaf surfaces. Two traits showed variation under different treatments: upper stomatal guard cell length (UP_GCL) and lower stomatal guard cell width (LOW_GCW). However, no differences in stomatal traits were found between lines tested under well-watered, drought-stressed, or plasticity conditions (Tables 1 and 2; Table S1).
The stomatal density on the upper side was lower than for the lower side in both well-watered and drought-stressed conditions. Moreover, in drought stress, the stomatal density decreased marginally on both leaf sides, accounting for 1.99% and 1.07% for adaxial and abaxial leaf surfaces, respectively.
There was a slight change in guard cell length and width under drought stress. In normal conditions, guard cell size did not differ, whether on the abaxial or adaxial sides. Guard cells on adaxial leaves increased in length and decreased in width by 7.13 and 2.06%, respectively, while both the length and width of guard cells located on abaxial leaves decreased by 1.67 and 10.17%, respectively (Tables 1 and 2). The guard cell length was used to calculate the maximum area of the open stomatal pore (UP_ Amax, LOW_ Amax). UP_Amax was slightly higher than LOW_Amax in normal conditions, at 31.09 um 2 and 29.94 um 2 , respectively. However, responses under drought stress for UP_Amax and LOW_Amax were different. There was a 15.29% increase in UP_Amax, while LOW_Amax slightly decreased by 2.74% (Tables 1 and 2).

Relationship among the Evaluated Parameters
Strong positive correlations among net photosynthesis, stomatal conductance, and transpiration rate in drought stress conditions and drought stress plasticity were observed ( Figure 1). IWUE was correlated with net photosynthesis rate in drought stress, while there is no correlation in plasticity ( Figure 1).

Relationship among the Evaluated Parameters
Strong positive correlations among net photosynthesis, stomatal conductance, and transpiration rate in drought stress conditions and drought stress plasticity were observed ( Figure 1). IWUE was correlated with net photosynthesis rate in drought stress, while there is no correlation in plasticity ( Figure 1). In addition, the net photosynthesis rates under drought condition had weak but insignificant correlations ( Figure 1) with QYmax, whereas QYmax was positively correlated with IWUE under the plasticity group of traits.
Correlations between plant height and stomatal conductance, transpiration rate, and IWUE were also found. Plant height was negatively correlated with stomatal conductance and transpiration rate but positively correlated with IWUE. The negative correlations under drought stress were stronger than for plasticity ( Figure 1).
A moderately positive correlation between the two stomatal densities was shown in drought stress conditions and plasticity ( Figure 1). Moreover, there was a positive correlation between stomatal density and IWUE under drought stress plasticity, while the abaxial side showed a higher correlation than the adaxial side. Meanwhile, the densities were also positively correlated to plant height ( Figure 1).
There was a negative correlation between upper guard cell length and transpiration rate (plasticity traits) (Figure 1). UP_ Amax was found correlated with the transpiration rate under drought plasticity ( Figure 1).

Principal Component Analysis (PCA)
A principal component analysis (PCA) was performed on all fourteen physiological and stomata morphological traits under drought stress and for drought stress plasticity. In addition, the net photosynthesis rates under drought condition had weak but insignificant correlations ( Figure 1) with QYmax, whereas QYmax was positively correlated with IWUE under the plasticity group of traits.
Correlations between plant height and stomatal conductance, transpiration rate, and IWUE were also found. Plant height was negatively correlated with stomatal conductance and transpiration rate but positively correlated with IWUE. The negative correlations under drought stress were stronger than for plasticity ( Figure 1).
A moderately positive correlation between the two stomatal densities was shown in drought stress conditions and plasticity ( Figure 1). Moreover, there was a positive correlation between stomatal density and IWUE under drought stress plasticity, while the abaxial side showed a higher correlation than the adaxial side. Meanwhile, the densities were also positively correlated to plant height ( Figure 1).
There was a negative correlation between upper guard cell length and transpiration rate (plasticity traits) (Figure 1). UP_ Amax was found correlated with the transpiration rate under drought plasticity ( Figure 1).

Principal Component Analysis (PCA)
A principal component analysis (PCA) was performed on all fourteen physiological and stomata morphological traits under drought stress and for drought stress plasticity. Three PCs were considered in both conditions. The three PCs explained almost 60 and 75% of the cumulative variance in all traits under drought stress and for drought stress plasticity, respectively ( Figure 2). Three PCs were considered in both conditions. The three PCs explained almost 60 and 75% of the cumulative variance in all traits under drought stress and for drought stress plasticity, respectively ( Figure 2). In drought stress, the first component (PC-1) giving 25.87% of the variance which include gs, E, HEIGHT and LOW_GCW. Both gs and E were in a similar direction, while other traits in PC-1 are in the opposite direction (Table S2) For drought stress plasticity, 35.81% of variance was on PC-1 which include gs, E, HEIGHT, UP_GCL, UP_ Amax, and LOW_SD. Both gs and E had a similar direction, while their direction was opposite of the other traits in PC-1. PC-2 consisted of Pn, IWUE, UP_SD, UP_GCW, LOW_GCL, and LOW_ Amax, giving 24.57% of variance. Almost all traits gave the same direction except for LOW_GCL and LOW_Amax, which had negative loading scores. Only two traits, QY_max and LOW_GCW, constituted PC-3, accounting for 13.90% of the variance. These two traits were negatively correlated to each other (Figure 2 and Table S2).
Overall, the pattern was almost similar in both drought stress and for drought stress plasticity.

Bulk Analysis
Bulk segregant analysis was used to select lines that performed good and bad under drought stress. Net photosynthesis rate was chosen as the selection trait since it directly affected biomass and yield production. Six out of ten KDML105-CSS lines were selected. The first three lines performed photosynthesis well in drought stress and have the highest drought stress plasticity. These three lines were CSSL62, CSSL28, and CSSL136. In contrast, the other three lines, namely CSSL119, KDML105, and CSSL29, displayed the lowest photosynthetic plasticity in (Figure 3). In drought stress, the first component (PC-1) giving 25.87% of the variance which include g s , E, HEIGHT and LOW_GCW. Both g s and E were in a similar direction, while other traits in PC-1 are in the opposite direction (Table S2) For drought stress plasticity, 35.81% of variance was on PC-1 which include g s , E, HEIGHT, UP_GCL, UP_ Amax, and LOW_SD. Both g s and E had a similar direction, while their direction was opposite of the other traits in PC-1. PC-2 consisted of P n , IWUE, UP_SD, UP_GCW, LOW_GCL, and LOW_ Amax, giving 24.57% of variance. Almost all traits gave the same direction except for LOW_GCL and LOW_Amax, which had negative loading scores. Only two traits, QY_max and LOW_GCW, constituted PC-3, accounting for 13.90% of the variance. These two traits were negatively correlated to each other ( Figure 2 and Table S2).
Overall, the pattern was almost similar in both drought stress and for drought stress plasticity.

Bulk Analysis
Bulk segregant analysis was used to select lines that performed good and bad under drought stress. Net photosynthesis rate was chosen as the selection trait since it directly affected biomass and yield production. Six out of ten KDML105-CSS lines were selected. The first three lines performed photosynthesis well in drought stress and have the highest drought stress plasticity. These three lines were CSSL62, CSSL28, and CSSL136. In contrast, the other three lines, namely CSSL119, KDML105, and CSSL29, displayed the lowest photosynthetic plasticity in (Figure 3).  Drought stress plasticity of the selected lines was analyzed using a t-test to investigate dynamics of different photosynthesis mechanisms among the top three best-performing and bottom three worst-performing lines. Violin plots clearly show that net photosynthesis rate was significantly different between these two groups (p < 0.01) (Figure 3). Moreover, there was a significant difference in stomal conductance and transpiration rate (p < 0.05). The lines which performed well in photosynthesis still showed better plasticity in gs and E than the bottom group ( Figure 3).
The means of maximum PSII quantum yield (QYmax) from the two groups was also significantly different (p < 0.05). The top group had higher QYmax drought stress plasticity than the bottom group. In contrast, the bottom group had high plasticity in plant height. Although there was no significant difference in intrinsic water-use efficiency (IWUE) between the top and the bottom group, the top group tended to have a higher IWUE in terms of plasticity ( Figure 3).
There were no significant changes in any stomata morphological traits. However, the top group had higher upper stomatal density and upper guard cell width, and the stomatal density in the lower surface tended to be lower than for the upper side. Meanwhile, the upper guard cell length and the upper maximum area of open stomatal pore plasticity were high in the bottom group (Figure 3).

Discussion
Rice uses phenotypic plasticity as one of its physiological mechanisms to survive Drought stress plasticity of the selected lines was analyzed using a t-test to investigate dynamics of different photosynthesis mechanisms among the top three best-performing and bottom three worst-performing lines. Violin plots clearly show that net photosynthesis rate was significantly different between these two groups (p < 0.01) ( Figure 3). Moreover, there was a significant difference in stomal conductance and transpiration rate (p < 0.05). The lines which performed well in photosynthesis still showed better plasticity in g s and E than the bottom group ( Figure 3).
The means of maximum PSII quantum yield (QYmax) from the two groups was also significantly different (p < 0.05). The top group had higher QYmax drought stress plasticity than the bottom group. In contrast, the bottom group had high plasticity in plant height. Although there was no significant difference in intrinsic water-use efficiency (IWUE) between the top and the bottom group, the top group tended to have a higher IWUE in terms of plasticity (Figure 3).
There were no significant changes in any stomata morphological traits. However, the top group had higher upper stomatal density and upper guard cell width, and the stomatal density in the lower surface tended to be lower than for the upper side. Meanwhile, the upper guard cell length and the upper maximum area of open stomatal pore plasticity were high in the bottom group (Figure 3).

Discussion
Rice uses phenotypic plasticity as one of its physiological mechanisms to survive drought stress. Stomata play an important role in gas exchange, which has a direct effect on traits, such as photosynthesis, stomatal conductance, and transpiration [24]. In this experiment, ten selected KDML105-CSS lines showed a highly significant difference in almost all physiological traits (P n , g s , E, and QYmax) between well-watered and drought stress conditions. However, only g s and E were significantly different among lines in drought stress. Moreover, their physiological performance based on plasticity displayed a similar pattern. Around a 40% decrease was found in net photosynthesis rate, stomatal conductance, and transpiration rate. Furthermore, there were highly significant correlations among these three parameters in both drought stress and drought stress plasticity (p > 0.01), indicating relationships in physiological mechanisms that have been previously identified [25]. In contrast, the decrease in the maximum PSII quantum yield (QY_max) only accounted for 9.8%, which was a one-fold decrease compared to the three other physiological traits. Similar to soybean [26], water stress decreased the maximum PSII quantum yield and was found to contribute to P n under stress, just like P n and QY_max sharing variations in PCA in this study. Under drought stress, rice attempted to conserve water by lowering stomatal conductance, as found in the study of Caine et al. [27], with the potential trade-off of reducing carbon assimilation. Moreover, we found that water-use efficiency slightly increased by 3.62% in the experiment, indicating altered drought stress performance.
According to the function of stomata, the microscopic pore plays a vital role in gas exchange, which directly governs physiological mechanisms inside plants [18,24,25]. We expected that the stomatal morphological traits would be modified to maintain internal activity in plants in a stressed environment. However, there was no significant difference in these traits, except for guard cell length at the adaxial side and width at the abaxial side. The guard cell grew 7.13 percent longer on the adaxial side. In contrast, the length was shortened by 1.67% on the abaxial side. It is surprising to observe an increase in guard cell length in the adaxial leaf surface because it is typical for the guard cell to decrease in size upon encountering water stress [28]. Similarly, there was a decrease in guard cell width, but it was noticeable on the abaxial side, which was 10.17%. In contrast, only 2.06% of the width was reduced on the adaxial side. The change in stomatal size led to the change in pore area. There was an increase in upper stomatal maximum pore area (UP_Amax) and a decrease in lower stomatal maximum pore area (LOW_Amax). An increase in UP_Amax contradicts the finding that smaller stomata can efficiently enhance photosynthesis and stomatal conductance, leading to higher yields [29]. However, some O. sativa did not show this modification of stomata size. Smaller stomata do not always provide advantages in photosynthesis, according to Zhang et al. [30], because smaller stomata correlate with lower CO 2 concentrations in leaves [30][31][32][33][34]. In terms of stomatal density, there was a small decrease on both sides. The adaxial and abaxial stomatal densities decreased by 1.99 and 1.07%, respectively. Although there was no significant correlation, the abaxial leaves showed an inverse relationship between stomatal density and stomatal size. This negative relationship between the two traits has been investigated in response to environmental fluctuations, showing the same relationship as found in our experiment [35][36][37]. Franks and Beerling [31] suggested that smaller stomatal sizes and higher stomatal densities are associated with higher maximum stomatal conductance to water. This finding contradicts previous findings that rice reduces stomatal density to maintain water use efficiency [16,28,38]. Hence, KDML105-CSSLs tended to decrease their stomatal density on both sides. They also decreased their guard cell width on both sides and guard cell length on the abaxial side, while guard cell length on the adaxial side significantly increased. The change on the abaxial side was similar to previous findings and likely promotes water conservation [37]. These different plasticity dynamics indicate distinct responses of stomata on different sides of the leaves [39,40].
Water-use efficiency had a strong positive correlation with net photosynthetic rate under both drought and non-drought conditions, but it was not significant for drought stress plasticity. From the plasticity results, although KDML-CSSLs lines maintained their IWUE, their net photosynthetic rate dramatically decreased by almost 40%. This indicates that there are other factors governing IWUE maintenance apart from photosynthesis [41,42].
Changes in physiological mechanisms and in stomatal morphology affected rice growth. Height was significantly reduced under drought stress. Moreover, there was variation in height, which is negatively correlated to stomatal conductance and transpiration rate. This relationship could explain the plastic modification of rice to enhance water transport abilities under drought stress since tall plants limit stomatal conductance, which leads to insufficient water for metabolic processes. Thus, rice can enhance their IWUE, which was shown by a significant positive correlation with height in this experiment [42][43][44][45]. The decline in height positively correlated to a decrease in stomatal density on both the adaxial and abaxial sides. This supports the view that decreases in stomatal density help to prevent water loss and maintain stomatal conductance functioning [43,44].
For both drought stress and drought stress plasticity, PCA revealed that stomatal conductance, transpiration rate, and rice height are always together in PC-1. Both stomatal conductance and transpiration rate showed a negative relationship with plant height. This result suggests that stomatal conductance had better performance when rice height was short [45,46], whereas net photosynthesis and water-use efficiency were contained together in PC-3 in both drought stress and drought stress plasticity conditions with the same polarity. This suggests that water use efficiency could be increased by improving carbon assimilation [47,48]. Stomatal traits such as upper stomatal density, upper guard cell width, lower guard cell length, and lower maximal pore area were combined in PC-2 under both drought stress and drought stress plasticity. They were also classified as drought stress plasticity in the same PC as net photosynthesis and water use efficiency. This supports the role of stomata adjustment in water conservation and among physiological mechanisms [49,50].
The physiological mechanisms inducing drought tolerance in KDML105-CSSLs were investigated using bulk analysis. This statistical approach selected and distinguished between the highest and lowest performing lines in net photosynthesis rate plasticity. Each group contained three KDML105-CSSL lines and was defined as the top (best-performing) and bottom (worst-performing) groups. Lines with a high value of plasticity meant a positive change or a slightly negative change in photosynthesis during drought stress. In the top group, there was noticeably high plasticity in photosynthesis. Moreover, other physiological traits, such as stomatal conductance, transpiration rate, and maximum PSII quantum yield, also displayed the same pattern, such that the top group performed well in those traits and was significantly different from the bottom group. Although the wateruse efficiency in the top group was higher than the bottom group, it was not statistically different. This is because all lines tried to maintain water usage under stressful conditions, but at different efficiencies. At the same time, rice height in the top group with high photosynthesis was noticeably shorter than in the bottom group, correlating with previous work [45,46]. On the adaxial side, the stomatal density of the top group showed an increasing trend, while the guard cell length and stomatal pore showed the opposite relationship. There was no obvious difference in variation on the abaxial side. This suggests that upper and lower stomata responded differently to drought stress [51].

Plant Materails and Growth Conditions
Ten KDML105-CSSLs showing tolerance under drought stress in field conditions were selected for this experiment. The KDML105-CSS lines were developed using the backcross breeding method with marker-assisted selection [22,23]. The seeds were obtained from the Innovative Plant Biotechnology and Precision Agriculture (APBT). KDML105-CSSLs (CSSL26; CSSL28; CSSL29; CSSL37; CSSL54; CSSL62; CSSL119; CSSL123; CSSL128; and CSSL136), KDML105, DH103, and DH212 seeds were incubated at 50 • C for five days. The seeds were cultivated in seed germination cups for a week. Germinated seedlings were transplanted to square plastic pots (16 × 16 cm top, 11 × 11 cm bottom and 20 cm height). The pots were placed using a completely randomized design (CRD) in the open greenhouse at Kasetsart University, Kamphangsaen Campus, Nakhon Pathom, Thailand. The soil type used in this experiment was similar to rice fields within the university, which have a clay form (65.7% clay, 23.3% silt, and 11.0% sand). Each pot contained 6 kg of soil. Water was added, and 50% soil moisture was maintained and measured by a Soil Moisture Meter (Lutron PMS-714 SOIL Moisture Meter IP65, Lutron Electronic Enterprise Co., Ltd., Taipei, Taiwan). The greenhouse had natural ventilation so that the atmosphere, temperature, and humidity represented field conditions. Weeds were controlled manually, and insects were sprayed with insecticides at 55 days after sowing (DAS). Fertilizer formulas 16-8-2 and 46-0-0 were mixed and applied at a rate of 1 g/pot (0.31 g N, 0.04 g P and 0.01 g K) at 18 and 32 DAS. The plants were continuously treated and then moved and placed on the conveyor at the Plant Phenomics Center for plant phenotyping at 46 DAS. The center also featured natural ventilation as described above. The drought stress began at 49 DAS, when plants were at the tillering stage, by withholding watering until the end of the experiment at 64 DAS, while there was always 500 mL of water in the control plots.

Physiological Traits
Physiological measurements were performed at 50, 53, 57, and 60 DAS. The LI-6400 XT Portable Photosynthesis System (LI-COR Biosciences, Lincoln, NE, USA) was used to collect gas exchange data. In the leaf chambers, the light intensity was set at 1000 µmol −2 s −1 PAR. The leaf temperature was set at 27 • C, which was equal to the Plant Phenomics Center's temperature. Atmospheric CO 2 levels were permitted. Measurements were performed on a second fully expanded leaf. Leaf measurements took approximately 2 min. The width of the leaves was collected to standardize trait values afterward. Three traits were directly obtained using LI-COR, namely net photosynthesis (P n ), stomatal conductance (g s ), and transpiration rate (E), while intrinsic water-use efficiency (IWUE) was derived from the ratio of P n to g s .

Plant Phenotyping
Plant phenotype was analyzed at the Plant Phenomics Center, Rice Research Center, Kasetsart University, Kamphangsaen Campus, Nakhon Pathom, Thailand. The PlantScreenTM Phenotyping Systems (Photon Systems Instruments (PSI), Drasov, Czech Republic). The system uses photographs to analyze plant phenotypes. The Chlorophyll Fluorescence Unit and RGB Imaging Unit were further used in the experiment. All plants were photographed five times by each unit throughout the experiment at 49, 54, 56, 61, and 63 DAS.
The Fv/Fm protocol from the PlantScreenTM Chlorophyll Fluorescence Imaging Unit was used for detecting photosynthetic potential using a FluorCam SN-FC800-200 camera. The distance between the camera and the plant was automatically adjusted. All plants were transferred to dark adaptation for 30 min before fluorescence image capture. Next, 720 × 560-pixel images were acquired, delivering four values of parameters related to photosynthesis. The data included minimum fluorescence in the dark-adapted state (F0), maximum fluorescence in the dark-adapted state (Fm), variable fluorescence in the dark-adapted state (Fv), and maximum PSII quantum yield (Fv/Fm or QY_max). The last parameter, QY_max, was used in this experiment to understand the fundamental mechanisms of photosynthesis under both conditions [52].
The PlantScreenTM RGB Imaging Unit includes a GigE uEye UI-5580SE-C-5 Megapixels QSXGA Camera with a 1/2" CMOS Sensor (IDS, Germany) and 2560 × 1920 resolution pixels. The adjustment of the distance between the camera and plants was automatic and depended on plant height. Images were captured in top and side views. In this experiment, only side-view photographs were used to measure plant height.

Stomatal Traits
Stomata were collected three times, representing three different conditions: before (46 DAS), during (57 DAS), and after (64 DAS) drought stress. Next, nail polish was directly applied on both the abaxial and adaxial sides of the leaves' central part while avoiding the midrib. The nail polish was then peeled off and pressed on the glass slide to observe stomatal traits.
Upper and lower stomatal densities (UP_SD and LOW_SD) were observed under a microscope at 40x magnification (Olympus BH2-RFCA Fluorescence Microscope). The number of stomata was counted manually in three different locations per sample, and then it was converted to stomatal density, or the number of stomata per mm 2 . After that, the epidermal leaf images were captured by a microscope at 40x magnification (Leica DM500, Leica, Germany) with 1600 × 1200 resolution. On both sides, the guard cell size represented by guard cell length (UP_GCL and LOW_GCL) and width (UP_GCW and LOW_GCW) were measured from the images via the straight line command in the ImageJ program (ImageJ 1.53i). The maximum area of the open stomatal pore (Amax) on two sides (UP_Amax and LOW_Amax) were calculated following Franks and Farquhar's equation [53,54].
Maximum area of the open stomatal pore: where GCL = guard cell length.

Statistical Analysis
The mean data directly collected from the control and drought stress conditions were used for statistical analysis. Furthermore, drought stress plasticity was also considered. Plasticity was derived from each replication of control and drought stress data according to the formula: Drought stress plasticity = (DSmean − WWmean)/WWmean (2) where DSmean = mean value in drought stress conditions. WWmean = mean value in well-watered conditions.
A one-way analysis of variance (ANOVA), analyzed by Genstat 21st Edition software, was performed to evaluate the mean, the least significant difference (LSD), and the coefficient of variation (CV) under both conditions. Significant differences in all traits between well-watered and drought stress conditions were analyzed using combined analysis (p < 0.05). The correlations between each trait were calculated using Pearson's correlation coefficient (p < 0.05) and analyzed by the 'corrplot' R package [55]. The principal component analysis was performed using 'foctoextra' R package [56]. While bulk analysis based on plasticity in net photosynthesis rate was analyzed using 'tidyr', 'plyr', 'dplyr' R packages to investigate how plants with a high or low net photosynthesis rate performed in other traits with respect to their physiological mechanisms under drought stress.

Conclusions
KDML105-CSSL modified their physiological responses to drought stress to maintain photosynthesis. Furthermore, photosynthesis was indirectly influenced by water conservation modifications, which subsequently affected biomass production. In particular, the selected KDML105-CSS lines with high net photosynthesis had their stomatal morphology modified to improve water-use efficiency by increasing density and stomatal pore depth at the adaxial surface while decreasing density and size at the abaxial leaf surface. Aside from stomatal morphology, plant height influences water-use efficiency under stress, and shorter height improves the IWUE as influenced by stomatal morphology changes.