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Author: H. Wang, J. Siopongco, L. Wade and A. Yamauchi Title: Fractal analysis on root systems of rice plants in response to drought stress Journal: Environmental and Experimental Botany ISSN: 0098-8472 Year: 2009 Volume: 65 Issue: 2-3 Pages: 338-344
Abstract: A fractal analytical method was used to examine the developmental responses of root systems in upland rice genotype CT9993-5-10-1-M (japonica) and lowland genotype IR62266-42-6-2 (indica) (abbreviated as CT9993 and IR62266, respectively) to soil water stress. The root systems were grown for one month in root boxes with 25 cm in length, 2 cm in width and 40 cm in depth, which were filled with soil. The root systems were sampled by following the needle-pin board method, and then spread on the transparent plastic films with nets after carefully washing out the soils. The two-dimensional images of root systems were digitized by using a scanner. The digitized images were used for analysis based on fractal geometry with the box-counting method. The reductions in shoot dry weight, photosynthesis rate and transpiration rate of IR62266 by soil drought were greater than those of CT9993. The change of fractal parameters in response to soil moisture conditions differed between the two rice genotypes. The values of fractal abundance (FA) and fractal dimension (FD) in well-watered IR62266 plants were larger than in CT9993. The value of FA of IR62266 was decreased more by drought stress than that of CT9993, indicating that the volume of soils explored by the whole root systems of CT9993 was maintained or less decreased in response to drought stress in comparison to IR62266. Moreover, the values of FD tended to increase in CT9993 while it tended to decrease in IR62266 in response to drought. These root responses detected by the fractal analysis in CT9993 may be advantageous for its extracting more water from drying soils, which explains its better growth under drought stressed condition.
Author Address: [email protected]
URL: http://dx.doi.org/10.1016/j.envexpbot.2008.10.002 http://researchoutput.csu.edu.au/R/-?func=dbin-jump-full&object_id=8234&local_base=GEN 01-CSU01
CRO Number: 8234
1 Fractal Analysis on Root Systems of Rice Plants in Response to
Drought Stress
Hong Wang1,4, Joel Siopongco2, Len J. Wade3, and Akira Yamauchi4 *
(1Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural
Sciences; Key Laboratory of Plant Nutrition and Nutrient Cycling Research, Ministry of
Agriculture; Beijing, 100081; 2International Rice Research Institute, Los Baños, Laguna,
Philippines; 3Charles Sturt University, E.H. Graham Centre for Agricultural Innovation,
Wagga Wagga NSW 2678, Australia; 4Graduate School of Bioagricultural Sciences, Nagoya
University; Chikusa, Nagoya 464-8601, Japan)
Author for Correspondence
Akira Yamauchi
Tel/Fax: + 81-52-789-4022
E-mail: [email protected]
2 Abstract
A fractal analytical method was used to examine the developmental responses of root systems in upland rice genotype CT9993-5-10-1-M (japonica) and lowland genotype
IR62266-42-6-2 (indica) (abbreviated as CT9993 and IR62266, respectively) to soil water stress. The root systems were grown for one month in root boxes with 25 cm in length, 2 cm in width and 40 cm in depth, which were filled with soil. The root systems were sampled by following the needle-pinboard method, and then spread on the transparent plastic films with nets after carefully washing out the soils. The two-dimensional images of root systems were digitized by using a scanner. The digitized images were used for analysis based on fractal geometry with the box-counting method. The reductions in shoot dry weight, photosynthesis rate and transpiration rate of IR62266 by soil drought were greater than those of CT9993. The change of fractal parameters in response to soil moisture conditions differed between the two rice genotypes. The values of fractal abundance (FA) and fractal dimension (FD) in well-watered IR62266 plants were larger than in CT9993. The value of FA of IR62266 was decreased more by drought stress than that of CT9993, indicating that the volume of soils explored by the whole root systems of CT9993 was maintained or less decreased in response to drought stress in comparison to IR62266. Moreover, the values of FD tended to increase in
CT9993 while it tended to decrease in IR62266 in response to drought. These root responses detected by the fractal analysis in CT9993 may be advantageous for its extracting more water from drying soils, which explains its better growth under drought stressed condition.
Key words: Drought stress; Fractal analysis; Rice; Root system architecture
3 1. Introduction
Rice, the world’s most important food crop, can experience soil drought at several different growth stages (Price and Courtois, 1999). It is estimated, about one third of world rice area is rainfed lowlands and mostly prone to drought (David, 1991; MacLean et al., 2002). Even for deep water rice, accounting for 11% of world rice area (David, 1991), its productivity is threaten by the scarcity of water in the seedling stage before floods (Evenson et al., 1996).
Moreover, upland rice, which is grown under aerobic conditions and rainfed fields without standing water and accounts for 13% of the world rice area (David, 1991), easily encounters drought stress (Tran Van Dat, 1986; Ahmadi, 2004).
Root growth and development is considered very important for rice to adapt to soil water deficit stress (O’Toole, 1982; Fukai and Copper, 1995; Price and Courtois, 1999; Price et al.,
2002; Wade et al., 1999; 2000; Kato et al., 2006; Wang and Yamauchi, 2006). The development of a deep and extensive root system in upland rice is one of drought-adaptation strategies, which enables the rice plants to access water at soil depth under upland conditions
(O’Toole, 1982; Kondo et al., 2003). Rainfed lowland rice is grown in bunded fields without irrigation where soil conditions may fluctuate from flooded and anaerobic to droughted and aerobic (Wade et al. 1998; Suralta and Yamauchi, 2008). Some rice genotype showed a high adaptation to such cycles with the ability of the roots to proliferate quickly prior to and during the early stages of drought (Bañoc et al., 2000; Kamoshita et al., 2000; Suralta et al., 2008).
Despite having fewer roots in deeper layers, rainfed lowland rice can extract water from soil depth below 15-cm in subsequent drought periods (Wade et al. 1999). Enhanced capacity of the root system to penetrate the hardpan is considered another key factor for drought adaptation of rice plants grown under rainfed lowland conditions (O’Toole, 1982; Fukai and
Copper, 1995; Wade et al., 1999).
Two rice genotypes, upland adapted japonica CT9993 and lowland adapted indica IR62266
4 were reported to show contrasting root morphology and adapting mechanisms in response to different water conditions. CT9993 shows low osmotic adjustment and is well adapted to rainfed lowland conditions with deep and thicker root system and strong root penetration ability (Wade et al., 2000; Azhiri-Sigari et al., 2000; Samson et al., 2002). IR62266 has a higher osmotic adjustment capacity (Lilley and Ludlow, 1996) and a shallow root system
(Azhiri-Sigari et al., 2000; Wade et al., 2000; Nguyen et al., 2004). A population of doubled-haploid lines (DHLs) derived from a cross of CT9993 and IR62266 were used to identify some QTLs related to root traits, such as root biomass, root thickness, root length, and root penetration (Zhang et al., 2001; Kamoshita et al., 2002a; 2002b; Nguyen et al., 2004), and the plasticity in lateral root development (Wang et al., 2005). Siopongco et al. (2005;
2006) used this population to identify the root and shoot developmental and physiological traits that are mainly responsible for adaptation to various soil water conditions. However, it still remains to be unclear how the entire root system of CT9993 and IR62266 respond to water stress.
The architecture of the root system is also well known to be a major determinant of root functions in the acquisition of soil resources such as nutrients and water (Lynch, 1995;
Yamauchi et al., 1996; Fitter, 2002; Wang et al., 2006). Fractal geometry is being widely applied to assess the root system architecture and the distribution of root systems in soils
(Fitter and Stickland, 1992; Berntson et al., 1997; Lynch and van Beem, 1993; Tatsumi et al.,
1989; Tatsumi, 1995; 2001; Masi and Maranville, 1998; Walk et al., 2004; Dannowski and
Block, 2005). Fractal geometry is a system of geometry that is more suitable for the description of complex natural objects than standard Euclidian geometry (Mandelbrot, 1983).
A fractal is an object having a non-integer dimension. Root systems also have self-similarity and are considered as the approximate fractal objects over a finite range of scales (Tatsumi et al., 1989). Fractal analysis in root biology often typically utilizes box counting method and
5 –D the equation: N(r) = Kr is obtained (Tatsumi et al., 1989; Tatsumi, 2001;Walk et al., 2004), where r is the length of the box side, and N(r) is the number of boxes of size r needed to cover
–D the object. In terms of fractal analysis, the equation: N(r) = Kr is transformed to the regression of log of N(r) intersected by roots vs. r levels. The slope (D) and intercept log K are computed. D is the fractal dimension (FD), and log K is associated with fractal abundance
(FA). The FD is closely related with the branching pattern of roots, while the FA with the volume of space explored by roots (Tatsumi et al., 1989; Tatsumi, 2001; Walk et al., 2004).
The FD is found to be correlated with root topology (Fitter and Stickland, 1992) and root architecture (Nielsen et al., 1997). The variation of FD has been noted among four species of dicots and monocots (Fitter and Stickland, 1992), as well as among genotypes of sorghum, rice and common bean (Izumi et al., 1995; Masi and Maranville, 1998; Nielsen et al., 1998).
Fitter (1994) showed that the increase of the volume of the soils explored by the roots, as a result of continuous branching, may reflect the plant's adaptive ability to make best use of unevenly distributed water and nutrients in soils. The analysis on the characterization of root system architecture would assist in better understanding the functional and growth strategy of root systems of rice plants when they are faced to insufficient supply of soil water
(Ketipearachchi and Tatsumi, 2000). Izumi et al. (1995, 1997) reported that soil moisture affected rice seminal root system development and its architecture. However, there have been only few studies about evaluation of the architecture of whole rice root systems in response to soil drought stress using the fractal analysis. In this study, we applied fractal analytical method to determine root system architecture of upland adapted japonica CT9993 and lowland adapted indica IR62266 under different soil water conditions, and compared the differences of fractal parameters for describing root architecture between these two rice cultivars in response to the soil water deficit stress.
6 2. Materials and methods
2.1. Plant materials and culture
Two rice genotypes, upland adapted japonica CT9993 and lowland adapted indica IR62266, were selected. The seeds were soaked in water and incubated in seed germinator maintained at
28 ℃ for 24 hours prior to sowing. Pre-germinated seed from each genotype was individually grown in root box with 25 cm in length, 2 cm in width and 40 cm in depth according to the methods developed by Kono et al. (1987).
After sieving in 2 mm mesh, 2.5 kg air-dried loamy sand soils mixed with 0.25 g of compound fertilizer (N 12%, P 16%, K 14%) were filled in each root box.
Root boxes were soaked in water pool until soils were saturated. Two soil moisture treatments were prepared in the experiment. In well-watered treatment, the soil was first submerged in the water for 24 hours followed by draining to maintain 25%-30% soil moisture content throughout the experimental period. The 25%-30% soil moisture content is close to field capacity of the used loamy sand soil (26%). In drought-stressed condition, the soils were also first saturated as mentioned above, watering was then withheld from planting until the sampling. As a result, soil moisture content gradually reduced to 10% at two weeks after planting, and maintained to 8% for the final two weeks. The 8%-10% soil moisture content was equivalent to 30%-38% of the field capacity. The soil moisture content was estimated by using gravymetric method. The reduced amount of soil water was added to maintain the soil moisture for each treatment. All the root boxes were placed under a vinyl house to grow plants. The experiment was set up in a randomized complete design with 3 replicates.
3.2. Sampling and measurements
The rice plants were grown in root boxes for four weeks. At harvest, the shoots were
7 detached from the roots and sampled. The shoot dry weight was then measured after drying at
70 ºC for 72 h.
The intact whole root systems were obtained following the needle-pinboard methods and the transparent plastic sheets with nets were used to fix and keep root samples in situ. This method enables us to sample the entire root system with nearly original orientation and distribution in soil with minimum damage and disturbance (Kono et al., 1987). Root samples were stored in FAA solution (70% ethanol: Formalin: Acetic acid = 18 : 1 : 1 parts by volume) prior to measurements. The root systems were then stained with 0.25% Coomassie Brilliant
Blue R aqueous solution to get clear contrast for computer images, which were digitized with the EPSON ES2200 scanner at 300 dpi resolution for further analysis. The total root length was measured with image analysis method proposed by Kimura et al. (1999) and Kimura and
Yamasaki (2003).
After removing the plastic sheets, the lengths of seminal and nodal root axes were directly measured by using ruler. Lateral root length was estimated as a difference between the total root length and the sum of the length of seminal root and nodal root axes. Total nodal root number was counted. After such measurements, root dry weight was determined after drying at 70 ºC for 72 h.
3.3. Fractal analysis
The digitized root images were used for fractal analysis following the box-counting method described by Tatsumi et al. (1989) and Ketipearachchi and Tatsumi (2000), who developed the macro program for fractal analysis within NIH 1.61 software (developed at the US National
Institutes of Health, http://rsb.info.nih.gov). The root image was first covered with the frame and the frame was divided into boxes (grids) with side length r. The size of boxes used was
8 designed from 1 to 320 pixels (0.254-81.28 mm) with 60 steps. The number N(r) of boxes that intersected with the image was counted. When plotting N(r) against r on a lg-lg scale, the power-law relationship N(r)=Kr-D was obtained if the images were fractal. The two constants
D and lgK were calculated based on the following equation lgN(r) = -Dlgr +lgK as the fractal parameters FD and FA, respectively.
3.4. Photosynthesis and related parameters measurements
The photosynthetic rate (PN), and transpiration rates (Tr), and stomatal conductance (gs) of the first fully expanded leaf near midday were measured with LI-6400 portable photosynthesis system (LI-COR Biosciences Inc.) at 25 day after sowing, when soil moisture treatments had been set for 10 days. The LI-6400 was operated in the open mode. External air was scrubbed of CO2 and mixed with a supply of pure CO2 to result in a reference concentration of 390 µl l-1. Flow rate was 500 µmol s-1. The temperature of sample cell was maintained at 25.7±0.1 ºC with cooler. The external air relative humidity was about 55%.
3.5. Statistical analyses
The data was analyzed following analysis of variance by using IRRISTAT (International
Rice Research Institute) software, and means were compared by Least significant difference
(LSD) Test at the P < 0.05 level. The correlative analysis was also performed.
9 3. Results
3.1. Plant biomass
The dry weights of shoots in both cultivars were reduced by soil drought. The more decrease in shoot dry weight was observed in IR62266 than in CT9993 (Table 1).The root biomass was not affected by water stress (Table 1).
3.2. Root traits
Under well-watered condition, the total root length was larger in IR62266 than in CT9993 mainly because of more number of nodal roots and greater lateral root length in the former
(Table 2). Drought significantly reduced the number and the total length of nodal roots, and the total length of lateral roots. The reduction of the total root length due to drought was greater in IR62266 than that in CT9993. Lateral roots accounted for the major portion of the entire root system in length especially under drought condition. On the other hand, the seminal root length in the drought-stressed plants of CT9993 decreased significantly, while that of IR62266 showed little change (Table 2).
3.3. The photosynthetic rate, transpiration rate, and stomatal conductance
CT9993 showed significantly higher level of photosynthetic rate (PN) than IR62266 under both well-watered and drought conditions, while there were no significant differences in transpiration rate (Tr) and stomatal conductance (gs) between the two rice cultivars (Table 3).
Drought stress substantially reduced PN by 18 %, Tr by 51 %, and gs by 67 % in CT9993, while the reduction was even more in IR62266, by 29%, 63%, and 75%, respectively (Table
3).
3.4. Fractal values
10 The fractal values of FD and FA from analyses in two dimension-planar intersections are presented in Table 4. Under well-watered condition, there was a significant correlation between FD and FA (R2=0.98**), while the correlation between FD and FA under drought condition was not significant (R2=0.10).
IR62266 had a higher FD value than CT9993 under well-watered condition. The drought tended to increase the FD value for CT9993 while it tended to decrease the FD value for
IR62266 although the changes were not significant, indicating that the responses in branching to dry soil may be contrasting between the two genotypes (Table 4).
The FA of well-watered IR62266 plants was larger than that of CT9993. Compared with well-watered condition, the FA values of both genotypes became significantly smaller under drought stress, indicating that soil drought reduced the volume of soils explored by the roots
(Table 4). Moreover, the more decrease in FA values was found in IR62266 than in CT9993.
3.5. Correlation between fractal values and root traits
The root traits that are related with elongation growth were found to be positively correlated with the fractal values. Specifically, the FD and the FA of well-watered plants were significantly correlated with total root length, total nodal root length and total lateral root length when the data of the two genotypes were pooled (Figure 1). In contrast, under drought-stressed condition, the FD did not show significant correlation with any of the roots traits examined while the FA was found to be positively correlated with seminal root length and total nodal root length.
Comparing the two genotypes by pooling data from well-watered control and drought treatments, in IR62266, the FD showed significant correlations with total root length, total lateral root length, and nodal root length (Figure 1) mainly because the root parameters significantly decreased (Table 2) and the FD also showed a tendency of decrease (Table 4) in
11 response to drought treatment. However, in CT9993, any of those root parameters did not show significant correlation with the FD value (Figure 1) because the former significantly decreased (Table 2) while the FD values tended to increase (Table 4) in response to drought.
By contrast, the FA value of CT9993 had significant correlation with all the root traits examined when pooling all data from well-watered and drought treatments. IR62266 showed similar trends although correlative relationships between the FA values and the length pf seminal root and total lateral roots were not significant.
On the other hand, the two fractal values showed no positive correlation with root initiation trait (nodal root number) when the data from the two genotypes were pooled. The correlative relationship between the FA and nodal root number of plants grown under drought-stressed condition was even significantly negative (Figure 1). In contrast, genetic comparison shows that the both of FD and FA values were significantly correlated with nodal root number in
IR62266 while only the FA values were so in CT9993.
12 4. Discussion
In this study, fractal analysis was used to quantify the root system architecture of rice plants grown under different soil moisture conditions. Under well-watered condition, lowland adapted rice genotype IR62266 had higher FD and FA values than upland rice CT9993, which coincides with the root characteristic of IR62266 having more number of lateral roots and nodal roots (Wade et al., 2000; Azhiri-Sigari et al., 2000; Kamoshita et al., 2000).
When plants were grown in soils with drought stress, the FA values of both genotypes decreased, indicating that the volume of soils explored by whole root systems was reduced by the shortage of water in soils. The number of lateral roots and nodal roots in IR62266 was more than that in CT9993, but the length of nodal and lateral roots in IR62266 decreased more largely when subjected to drought (Table 2), which may result in more reduction in the volume of soils explored by roots (expressed by the FA value) in IR62266 than CT9993.
The maintenance or less decrease of the volume of soils explored by roots may benefit
CT9993 to extract more soil water and better adapt to water deficit stress, which was reflected in less reduction of this genotype in stomatal conductance, photosynthetic rate (Table 3) and shoot biomass (Table 1) in response to drought treatment as compared with IR62266. These results were consistent with other studies which reported that CT9993 was well adapted to water deficit conditions in rainfed lowlands following an avoidance strategy with deep and thicker root system and strong root penetration ability (Wade et al., 2000; Azhiri-Sigari et al.,
2000; Samson et al., 2002). In contrast, IR62266 followed a tolerance strategy favoring osmotic adjustment and had a shallow root system with slower access of available water at depth (Wade et al, 2000; Azhiri-Sigari et al., 2000; Kamoshita et al., 2000; 2004).
The other fractal parameter, FD levels of IR62266 and CT9993 showed contrasting trends in response to water stress. Specifically, FD value of IR62266 tended to decrease while that of
CT9993 tended to increase although the changes were not significant. Tatsumi (1995)
13 reported that the FD value was not determined by the length or area of root system but was closely related to the branching order or density of lateral roots. Izumi et al. (1995) found that the complexity of seminal root morphology in rice plants characterized by fractal dimension had a close relation with the initiation and elongation of S-type (short and non-branching) first order lateral roots (Yamauchi et al., 1996), which accounted for the major part of total number of lateral roots in the entire root systems. Increased FD value therefore may indicate the changes in pattern and degree of branching, and more specifically, promoted branching.
Additionally, lateral roots were found to account for the major parts of root system especially under drought condition (Table 2). These facts suggest that as compared with IR62266,
CT9993 may have an ability to promote or at least maintain lateral root branching in response to soil drying, to which less reduction in total root length, plant dry weight, and related physiological parameters may also be attributed. This experiment used one-month-old seedlings and we did not perform detailed measurements on lateral root branching, and so further work is needed to examine the developmental parameters of root branching, and the responses of fractal parameters in larger or more aged rice plants or in field-grown rice plants under different soil moisture conditions.
For further interpretation of fractal values, they were correlated with various root parameters (Figure 1). The FD values showed significant correlations with the length of the whole root system, lateral and nodal roots in IR62266 while they showed no correlation with any of them in CT9993 (Figure 1). As stated above, this was mainly due to the fact that both
FD value and root length parameters showed similar decreasing trends in response to drought in IR62266 while the FD value did not follow the decreasing trends shown by root parameters in case of CT9993. Therefore, such changes found in FD values can well characterize the root responses of each genotype in addition to size (length) changes, which may be related with lateral root branching. In addition, when the data from the two genotypes were pooled, the
14 values were significantly correlated with those root traits only for well-watered plants but not for droughted plants. These facts also indicate that the two genotypes may differ in response of branching to soil drought conditions.
The FA values were found to be significantly correlated with the total root length that represents root system size in respective genotype when the data from the two different soil conditions were pooled. This fact further supports the view that the FA is closely related with the soil volume explored by the root system, which has been proposed by other studies
(Tatsumi et al., 1989; Tatsumi, 2001; Walk et al., 2004) and was confirmed by this study as pointed out earlier. As found in FD values, when the genotypes were pooled, these FA values were also correlated with the root length traits under well-watered conditions but the correlations were not significant under drought condition although the r values tended to be higher than in the case of FD values (data not shown). This was largely because of substantially reduced variations in the root system size among the genotypes and replicates due to the drought treatment of this experiment. This study showed the necessity to further accumulate the evidence on the relationship between fractal values and developmental parameters of roots grown under different conditions for more precise interpretation of the functional significance of the fractal values.
Fractal dimensions can also be calculated based on interception of three-dimensional boxes by the roots (Eshel, 1998; Walk et al., 2004). This kind of fractal dimension is called mass fractal (Obert et al., 1990) because it relates to the volume occupied by the object, but not to its perimeter alone. It would be of great interest to perform fractal analysis at three-dimensional level to evaluate the functional significance of the root system responses to soil water stress. Moreover, double haploid lines have been produced from these genotypes, and the further study is now in progress, which examines the genetic regulation of these root responses to different moisture conditions.
15 Acknowledgments:
This research was supported by the Japan Society of the Promotion of Science with a Grant in Aid for Scientific Research (No. 19380011) and . This research was also partially supported byfrom National Basic Research Program of China (No. 2007CB109302). We are grateful to three anonymous reviewers for valuable comments on the manuscript.
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22 Table 1 Dry weights (g plant-1) of plants grown under drought-stressed and well-watered conditions. Genotypes Treatments Root Shoot Total plant Drought-stressed 0.23 a 0.46 a 0.69 a CT9993 Well-watered 0.18 a 0.66 b 0.84 a D/W 1.23 0.70 0.82 Drought-stressed 0.22 a 0.42 a 0.64 a IR62266 Well-watered 0.23 a 0.90 c 1.13 b D/W 0.96 0.47 0.57 LSD value Water× Genotype 0.06 0.17 0.21
Means within a column flanked by the same letter are not significantly different at P=0.05 level. D/W=Ratio of values in drought-stressed to well-watered plants.
23 Table 2 The length (cm plant-1) of various types of roots and nodal root number (roots plant-1) of plants grown under drought-stressed and well-watered conditions. Total root Seminal Nodal root Total nodal Total lateral Genotypes Treatments length root length number root length root length Drought- 1722.8 a 33.0 a 16.0 a 142.2 a 1547.5 a (90) stressed CT9993 Well-watered 2834.7 b 43.2 b 31.0 b 588.8 b 2202.7 b (78) D/W 0.61 0.76 0.5 0.24 0.70 Drought- 1628.6 a 35.0 a 17.3 a 120.1 a 1473.5 a (90) stressed IR62266 Well-watered 3929.4 c 38.5 ab 68.7 c 1086.4 c 2804.5 b (71) D/W 0.41 0.91 0.26 0.11 0.53 LSD Water× 737.9 7.7 6 151.5 649. 6 value Genotype Means within a column flanked by the same letter are not significantly different at P=0.05 level. D/W=Ratio of values in drought-stressed to well-watered plants. Values in the parentheses in total lateral root length show the percentage of the length by lateral roots in that of entire root system (Total lateral root length×100/Total root length).
24 Table 3 The photosynthetic rate (PN), transpiration rate (Tr), and stomatal conductance (gs) in two genotypes of rice plants grown under drought-stressed and well-watered conditions. P T g Genotypes Treatments N r s (μmol m-2 s-1) (mmol m-2 s-1) (mol m-2 s-1) Drought-stressed 18.97 b 4.16 a 0.19 a CT9993 Well-watered 23.27 c 8.56 b 0.57 b D/W 0.82 0.49 0.33 Drought-stressed 13.27 a 2.85 a 0.12 a IR62266 Well-watered 18.67 b 7.64 b 0.48 b D/W 0.71 0.37 0.25 LSD value Water× Genotype 3.59 1.37 0.15 Means within a column flanked by the same letter are not significantly different at P=0.05 level. D/W=Ratio of values in drought-stressed to well-watered plants.
25 Table 4 Fractal valures (FD and FA) of roots in two genotypes of rice plants grown under drought-stressed and well-watered conditions. Genotypes Treatments FD FA Drought-stressed 1.66 ab 6.07 a CT9993 Well-watered 1.63 a 6.28 b D/W 1.02 0.97 Drought-stressed 1.65 ab 6.07 a IR62266 Well-watered 1.69 b 6.46 c D/W 0.97 0.94 LSD value Water× Genotype 0.04 0.15 Means within a column flanked by the same letter are not significantly different at P=0.05 level. D/W=Ratio of values in drought-stressed to well-watered plants.
26 Figure captions
Figure 1 Relationship between the fractal values (FD, left column and FA, right column) and various root traits of rice plants grown under well-watered (triangle) and drought stressed (square) conditions. Data of both CT9993(opened symbol) and IR62266 (closed symbol)were included in each graph. Significant Pearson’s correlation coefficients with significance level were denoted by * P <0.05, ** P <0.01.
27 Figure 1
1.82
1.78 6.6 6.5 1.74 2 6.4 R = 0.149 1.70 6.3 2 2 ** R = 0.555 R = 0.902 1.66 6.2
Fractal Fractal dimension 6.1 2 ** 1.62 R = 0.907 6.0 Fractal abundance 1.58 5.9 5.8 500 1500 2500 3500 4500 500 1500 2500 3500 4500 Total root length (cm) Total root length (cm)
1.72 6.6 6.5 1.70 2 2 R = 0.090 1.68 R = 0.052 6.4 6.3 1.66 6.2 2 * 1.64 R = 0.750 6.1 2
Fractal Fractal dimension R = 0.219 1.62 Fractal abundance 6 1.60 5.9
1.58 5.8 25 30 35 40 45 50 25 30 35 40 45 50 Seminal root length (cm) Seminal root length (cm)
1.72 6.6 R2 = 0.852* 1.70 6.5 R2 = 0.141 1.68 6.4
2 * 6.3 1.66 R = 0.869 6.2 1.64 6.1
Fractal Fractal dimension 1.62
Fractal abundance 6.0 2 1.60 5.9 R = 0.404 1.58 5.8 1000 1500 2000 2500 3000 3500 1000 1500 2000 2500 3000 3500 Total lateral root length (cm) Total lateral root length (cm)
28
1.72 6.6 2 6.5 1.70 R = 0.040 6.4 1.68 R2 = 0.678* 6.3 1.66 2 * 2 * R = 0.690 6.2 R = 0.709 1.64 6.1 1.62 Fractal dimension
Fractal Fractal abundance 6.0 1.60 5.9 1.58 5.8
0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200
Total nodal root length (cm) Total nodal root length (cm)
6.6 1.72 2 6.5 1.70 R = 0.021 6.4 1.68 6.3 2 * 1.66 R = 0.631 R2 = 0.611 6.2 R2 = 0.577 1.64
6.1 Fractal Fractal abundance Fractal dimension 1.62 6.0 1.60 5.9 1.58 5.8
5 10 15 20 25 30 35 40 45 50 55 60 65 70 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Nodal root number Nodal root number
29