Genetic analysis of drought resistance in rice by molecular markers: association between secondary traits and field performance. (Cell Biology & Molecular Genetics).
Several putative traits contributing to drought resistance in rice have been suggested (Fukai and Cooper, 1995). Root characteristics such as thickness, depth of rooting, root length density, root pulling force (RPF), and root penetration ability have been associated with drought avoidance in rice (Nguyen et al., 1997). Osmotic adjustment (OA) capacity is an important, shoot-related component of drought tolerance in crop plants. OA, defined as the active accumulation of solutes during the development of water stress in plants (Blum, 1988), allows maintenance of higher turgor potential at a given leaf water potential. OA delays leaf rolling, tissue death, and leaf senescence under water stress in rice (Hsiao et al., 1984) and has been shown to enhance grain yield under water limited conditions in several other crops (Zhang et al., 1999a). However, a yield benefit due to OA is yet to be demonstrated in rice. Despite our increased understanding of the role of putative traits in drought resistance, these traits are rarely selected for in crop improvement programs because phenotypic selection for most root traits and OA is difficult and labor intensive. Considering these limitations to efficient selection, molecular marker technology is a powerful tool for selecting such traits. QTLs have been detected for several root-related traits and OA in rice (Champoux et al., 1995; Lilley et al., 1996; Ray et al., 1996; Price and Tomos, 1997; Yadav et al., 1997; Ali et al., 2000; Price et al., 2000; Zheng et al., 2000; Zhang et al., 2001). A significant proportion of the phenotypic variability of several of these putative drought resistance traits is explained by the segregation of relatively few genetic loci, thus leading to the possibility of indirect selection of these complex traits by means of marker-assisted selection (MAS) strategy.
Although previous analysis indicated the map positions of QTLs associated with drought resistance traits, the effects of those traits on plant production under drought has not yet been established. Thus there is a need to determine whether the QTLs linked to drought resistance traits also affect yield under stress. By comparing the coincidence of QTLs for specific traits and QTLs for plant production under drought, it is possible to test whether a particular constitutive or adaptive response to drought stress is of significance in improving field level drought resistance (Lebreton et al., 1995). Such associations would also improve the efficacy of MAS in breeding for drought tolerance in rice. Thus, DH lines developed from two rice lines, differing in root traits and OA, were used in this study to identify the QTLs linked to rice performance under drought and to genetically dissect the nature of association between drought resistance traits and yield under drought in the field. The specific objectives of the present study were (i) to identify genomic regions linked to plant water stress indicators, phenology, and production traits under drought stress in the field; (ii) to establish the nature of phenotypic and genetic association between various physio-morphological traits and rice performance under drought; and (iii) to identify useful QTLs for improving drought resistance in rice.
MATERIALS AND METHODS
The rice breeding lines, CT9993-5-10-1-M and IR62266-42-6-2 differ consistently for a range of traits (Babu et al., 2001; Zhang et al., 1999b). These include gross root morphology, root penetration index (RPI), RPF, and OA. A DH line population was developed through anther culture from a cross between CT9993-5-10-1-M (abbreviated as CT9993, an upland japonica ecotype possessing a deep and thick root system and low OA) and IR62266-42-6-2 (abbreviated as IR62266, an indica ecotype with a shallow root system and high OA) at Centro Internacional de Agricultura Tropical (CIAT), Colombia, and International Rice Research Institute (IRRI), Philippines. Of the 220 DH lines of the population, 154 were used in this study.
Three separate field trials were conducted under upland conditions in experimental fields of Tamil Nadu Agricultural University, India, at two different locations: Trial 1 at Coimbatore during 1999 wet season (July-December), Trial 2 at Paramakudi during 1999-2000 wet season (September-February), and Trial 3 at Coimbatore during 2000 dry season (February-June). The main soil and drought stress characteristics of the trials are summarized in Table 1. In all the trials, the DH lines and their parents were evaluated under two water regimes: fully irrigated (nonstress) control and water stress under a randomized complete block design. Both the treatments were replicated three times in Trial 1, whereas in Trials 2 and 3 the irrigated control had two replications and the stress treatments had three replications. Experimental plots were 2 m x 0.60 m in Trials 1 and 3, and 2 x 0.4 m in Trial 2. There were 20- and 10-cm spacing between and within rows, respectively. A buffer channel 1.0 m wide and 0.75 m deep along the length of the experimental plot divided the control and stress plots in trial 1 and 3. In trial 2, the control and stress plots were separated by 3.5 m. Seeds were hand-dibbled into dry soil at 100 kg [ha.sup.-1]. NPK fertilizers were applied at a rate of 120:40:40 kg [ha.sup.-1]. While P and K were applied in full at the time of sowing, N was applied in four splits as top dressing. Plants were thinned to 50 hills [m.sup.-2] soon after emergence. Insect and weed control measures were applied periodically as required. In Trials 1 and 3, all plots were surface irrigated to field capacity once a week, except when water stress was imposed by withholding irrigation to stress plots from 63 and 83 d after sowing (DAS), respectively. In Trial 2, the control plots were surface irrigated, while the stress plots were rainfed from sowing to harvest.
Trial 1. Changes in soil moisture and penetration resistance were monitored periodically in stress plots with gravimetric measures and a penetrometer, respectively. Leaf relative water content (RWC) was determined at midday, 15 d after withholding irrigation, in youngest expanded leaf (Barrs and Weatherley, 1962). Two days later, leaf rolling and drying scores were made at midday on a 1-to-7 scale standardized for rice (IRRI, 1996). There was continuous stress for 25 d between 63 and 88 DAS. Stress was relieved by 16 mm rain 89 DAS and thereafter both control and stress plots were regularly irrigated until maturity. Days to heading, plant height, biomass and grain yield were recorded. All the plants in each plot were sampled for determining biomass and grain yield. Three panicles per DH line per plot were sampled to obtain data on number of grains per panicle, percent spikelet fertility and 1000-grain weight. Spikelet fertility was calculated as the ratio of matured grains to total spikelets per panicle. Harvest index was measured as the ratio of grain weight to total plant dry weight.
Trial 2. Leaf rolling score was recorded at midday, 12 d after cessation of rain. Plants were harvested at maturity. Days to heading, plant height, biomass, grain yield, and spikelet fertility were recorded in stress (rainfed) and irrigated control treatments.
Trial 3. Leaf rolling and drying scores were taken at midday 16 d after withholding irrigation. Leaf RWC and canopy temperature were determined midday, 17 and 19 d after withholding irrigation, respectively, in 40 DH lines at random and in parents. Canopy temperature was measured using a hand-held infrared thermometer (Model AG-42, Telatemp Corporation, Inc., Fullerton, CA, USA) as described by Garrity and O'Toole (1995). Stress was relieved 33 d after withholding irrigation by 14 mm rain. Plants were harvested 130 DAS and total above ground biomass was recorded. The plants did not reach maturity, since few DH lines flowered by 130 DAS and most panicles that exserted under stress were sterile. Days to heading was recorded for only the DH lines which flowered by 130 DAS.
Relative yield and relative biomass were calculated as yield and biomass under drought as a percentage of yield and biomass in control, respectively, in all the three trials.
Analyses of variance (ANOVA) were performed to check the genetic variance among the DH lines for all traits. The broad sense heritabilities (H) were then computed from the estimates of genetic ([[sigma].sup.2]G) and residual ([[sigma].sup.2]e) variances derived from the expected mean squares of the analysis of variances as H = ([[sigma].sup.2.sub.G]/([[sigma].sup.2.sub.G] + [[sigma].sup.2.sub.e]/k), where k was the number of replications. Phenotypic correlations among the traits within a trial were computed using the genotypic means.
Linkage Map and QTL Analysis
A genetic linkage map revised from a previous map (Zhang et al., 2001) consisting of 280 marker loci including 134 restriction fragment length polymorphisms (RFLPs), 131 amplified fragment length polymorphisms (AFLPs), and 15 simple sequence repeats (SSRs) was constructed on the basis of the 154 DH lines by means of MAPMAKER/Exp version 3.0. R1G1 was a cDNA fragment cloned via a differential display procedure homologous to water stress induced mRNA in rice. TGMSP2 was the DNA marker tightly linked to the thermo-sensitive genic male-sterile gene in rice. The map covered 1602 centimorgans (cM) in length on the basis of the Kosambi function with an average distance of 5.7 cM between adjacent markers. Using the genetic linkage map, we identified the QTLs linked to traits such as plant water relations, phenology, biomass, and yield using QTLMapper version 1.0 software (Wang et al., 1999a; 1999b). The threshold LOD score used to declare the presence of QTLs was 2.85, which was derived on the basis of the total map distance and average distance between markers according to Lander and Botstein (1989). Tests for independence of QTLs were conducted when two or more QTLs of the same trait within a trial were located on the same chromosome as described by Paterson et al. (1988).
RESULTS AND DISCUSSION
Variation in Plant Water Stress Indices, Phenology, and Production Traits under Stress
The phenotypic means of the population and its parents for the various traits from the three trials along with broad-sense heritabilities are summarized in Table 2. Water stress occurred in all the trials, although the severity of stress differed as indicated by the mean values for DH lines of RWC, leaf rolling and leaf drying scores, and days to heading under stress. Water stress was severe in Trial 1 with a continuous stress period of 25 d from 63 to 88 DAS. During the first 15 d of stress, there was 89% depletion of available soil moisture from field capacity and the soil strength increased from 0.27 to 3.10 MPa, both in the 10-to 20-cm soil layer. Mean leaf RWC across the DH lines declined to 68% under stress and in one DH line it was as low as 33%. The average of leaf rolling and drying scores across the DH lines was 5.7 and 4.8, respectively. CT9993 had higher RWC and lower drought scores compared to IR62266. Mean heading date was delayed by 14 d under stress. Heading date was delayed by 12 and 15 d in CT9993 and IR62266, respectively, under stress. Mean plant height was reduced by 3.8 cm under stress. While CT9993 did not have any reduction in plant height, IR62266 had 4.2 cm reduction in plant height under stress. The water stress caused an average reduction of 45% in biomass and 67% in grain yield. There was 13, 15, and 10% reduction in spikelet fertility, number of grains per panicle and harvest index, respectively, under stress. However, 1000-grain weight had a nonsignificant reduction under stress. The relative yield under drought ranged from 9 to 90% with a mean of 34% across the DH lines.
Transgressive segregation in both directions was observed for most traits. The frequency distribution of phenotypes for the traits evaluated in this study approximately fitted a normal distribution as shown for three traits for Trial 1 (Fig. 1). Broad-sense heritability of leaf rolling, leaf drying and days to heading under stress was high (0.65, 0.71, and 0.70, respectively), while that of RWC, plant height, grain yield, biomass, spikelet fertility, grains per panicle, and 1000-grain weight under stress was low to moderate.
[FIGURE 1 OMITTED]
Water stress was mild in Trial 2 compared to Trial 1, as there were only 12 consecutive days without rain, beginning 85 d after emergence. While leaves of CT9993 did not roll, IR62266 had a leaf rolling score of 3.7 under stress. The average leaf rolling score was 2.6 across the DH lines (Table 2). There were no symptoms of leaf drying in this trial. Stress delayed heading by 8 d on average, while mean plant height was reduced by 10.2 cm across the DH lines. Average biomass and yield were reduced by 43 and 47%, respectively, under stress. The broad-sense heritabilities were relatively high for most traits except percent spikelet fertility under stress.
Water stress was very severe in Trial 3 with a continuous stress period of 33 d from 83 to 116 DAS. During the first 22 d after withholding irrigation, 70% of available soil moisture below 20-cm soil depth was depleted from a full profile and the soil strength increased from 0.27 to 3.78 MPa. Mean leaf rolling and drying scores were 4.6 and 3.7, respectively across the DH lines. There was 59% reduction in biomass under stress. Mean RWC was 55% and that of canopy temperature was 33.8[degrees]C across 40 DH lines. CT9993 had higher RWC, lower drought scores and cooler canopy temperature compared to IR62266. Heading was delayed by 5 d in CT9993 under stress. However, IR62266 did not flower until the time of sampling (130 DAS) under stress compared to 110 d to heading in the control. The broad-sense heritabilities were relatively high for most traits in Trial 3 (Table 2).
In summary, there was a significant genotypic effect for most traits except for percent spikelet fertility under stress in Trial 2 and days to heading under stress in Trial 3. Significant differences for plant phenology and production traits under control and water stress conditions and for indicators of plant water stress have been reported among a subset of 100 of these DH lines (Blum et al., 1999).
Relationship between Water Stress Indices and Rice Production under Drought Stress
A major problem for direct selection under drought is the management of experimental conditions. There is high probability that a genotype performing well under control conditions will also perform well under drought, even if the relative yield reduction for this genotype is large, because of spillover effects of yield potential (Blum, 1988). Stress yield and control yields were significantly correlated across DH lines (r = [0.51.sup.**] and [0.34.sup.**] in Trial 1 and 2, respectively). Similar relation between potential yield in control and yield under water stress was reported in rice (Blum et al., 1999). Drought tolerance of the DH lines can be assessed by several parameters, namely yield (or biomass) under drought, yield under drought as a percentage of yield in control (relative yield), and drought susceptibility index (Fischer and Maurer, 1978). Relative yield (or biomass) is used as an index of drought tolerance in this study as also done by others (Ribaut et al., 1997; Blum et al., 1999). Relative yields under stress were negatively correlated with actual yields in the control in Trial 1 and 2 among the DH lines (data not shown). Thus, the DH lines performing best in control conditions had the most marked yield reduction under drought. On the other hand, relative yields under stress were very significantly positively correlated with actual yields under drought (r = [0.68.sup.**] and [0.56.sup.**], respectively in Trial 1 and 2) among the DH lines. Therefore, absolute yield of the DH lines under stress represents quite well their relative drought tolerance, despite its association with potential yield in the control. Similar results were reported in rice (Blum et al., 1999). Similar correlations between relative yields under stress and actual yields under stress and in control conditions have been reported in maize (Ribaut et al., 1997).
The phenotypic correlations between traits showed that parameters of water stress indicators were significantly correlated with plant phenology and production traits under stress in all the three trials. The correlation coefficients (r) among various traits under drought stress were presented for Trial 1 (Table 3), since it was considered comprehensive in terms of data collection from this series of field trials. In addition, out of the two trials, Trial 1 and 2 wherein data on yield were recorded, Trial 1 had severe water stress with an average yield reduction of 67% under drought compared with control. A yield reduction of more than 50% under stress is considered critical for the expression of drought resistance mechanisms in rice (Pantuwan et al., 2002). Leaf RWC was negatively correlated with leaf rolling and days to heading under stress. Leaf drying scores had negative correlations with yield and harvest index under stress (Table 3). Grain yield in control was not correlated with leaf RWC, leaf rolling, and drying scores determined under water stress, as normally expected (data not shown). Biomass under stress was positively correlated with yield, percent spikelet fertility, number of grains per panicle, harvest index, and relative yield under stress (r = [0.74.sup.**], [0.35.sup.**], [0.45.sup.**], [0.40.sup.**] and [0.41.sup.**], respectively). Percent spikelet fertility and harvest index under stress were positively correlated with relative yield under stress (r = [0.31.sup.**] and [0.68.sup.**], respectively).
Although the stress was relatively mild in Trial 2, plant water stress indicators were correlated with plant production traits under stress (data not shown). Biomass under stress was correlated with actual yield and relative yield under stress (r = [0.78.sup.**] and [0.42.sup.**], respectively). Actual yields under stress were positively correlated with relative yields under stress (r = [0.56.sup.**]).
In Trial 3, the severe and long (33 d) stress period coincided with flowering stage (days to heading ranged from 87 to 118 under control among the DH lines, Table 2), and heading either did not occur before 130 DAS or the panicles were sterile. Therefore, biomass was the only and best measure of plant production under stress in this trial. As in Trials 1 and 2, plant water stress indicators were correlated with biomass under stress in this trial (data not shown). Leaf RWC was negatively correlated with leaf rolling, leaf drying, canopy temperature, and days to heading under stress (r = [-0.58.sup.**], [-0.58.sup.**], [-0.38.sup.*], and [-0.44.sup.*], respectively). Canopy temperature had a positive correlation with leaf rolling and drying scores (r = [0.34.sup.*] and [0.33.sup.*], respectively). Drought scores were positively correlated with days to heading under stress. Biomass under stress was negatively correlated with canopy temperature (r = [-0.69.sup.**]).
These correlations between plant water status indicators and plant phenology and production traits under stress in this study confirmed the earlier reports in rice (Blum et al., 1999).
QTLs Linked to Plant Water Stress Indicators, Phenology and Production Traits
A total of 47 QTLs, significant at a LOD score of [greater than or equal to] 2.85, were identified for various plant water relations, phenology and production traits under control and stress conditions from the three field trials (Table 4). The number of QTLs identified for each trait within a trial varied from one to four with the proportion of explained phenotypic variation ([R.sup.2]) ranging from 5 to 59%. The QTL, rwc9.1 explained the highest proportion of the phenotypic variation (59%) for leaf RWC under stress in Trial 3. QTLs linked to various traits from the different trials were located throughout the genome except chromosome 5 (Fig. 2). QTLs for different traits were mapped to similar chromosomal locations within a trial. For example, phs4.1, gys4.1 and gpps4.1 were mapped to the RG939-RG476-RG214 region on chromosome 4 in Trial 1. Similarly, lr1.1, ld1.1, phc1.1, phs1.1, and gppc1.1 were mapped to the EM11_11-RG109-ME10_14 region on chromosome 1 in Trial 1, corresponding to the sd-1 semidwarfing locus (Price et al., 2000). QTLs, lr8.1 and his8.1 were mapped to the ME6_13-G187-ME2_11 region on chromosome 8 in Trial 1. Common QTLs across trials and water regimes were also detected for a given trait. For example, phs1.1 and phc1.1 were consistent across trials and water regimes. In all the trials, for traits related to higher plant water status and production under stress, the majority of the favorable alleles came from CT9993, the japonica parental line. The indica accession, IR62266 contributed most of the alleles for leaf rolling, leaf drying and delay in days to heading under stress. However, favorable alleles from IR62266 also contributed to plant production in terms of biomass, yield, harvest index, and relative yield under stress.
[FIGURE 2 OMITTED]
Comparison of QTLs for Root Traits and Rice Performance under Drought Stress
Understanding the genetic basis of drought resistance in crops is fundamental to enable breeders and molecular biologists to develop new cultivars with drought resistance. Physiological studies have indicated that the ability of the root system to provide for evapotranspirational demand from deep soil moisture and the capacity for OA are major drought resistance traits in rice (Nguyen et al., 1997). However, the association between variation in such quantitatively inherited traits and, ultimately, the effects of those traits on rice production under drought in the field has not been established. By comparing the coincidence of QTLs for specific traits and QTLs for yield under drought stress it is possible to test more precisely whether a particular trait is of significance in improving drought resistance (Lebreton et al., 1995). This can be done most simply by looking for coincidence of QTLs for two traits. Previous studies have mapped QTLs for RPI, root thickness at stem base (BRT), RPF, root morphology, and OA capacity in these DH lines (Zhang et al., 2001; Kamoshita et al., 2002). On comparing the locations of the QTLs linked to root traits and plant production traits under drought stress, the genomic region, RG939-RG476-RG214 on chromosome 4 was found to be significant in terms of drought resistance in rice. The QTLs, phs4.1, gys4.1, and gpps4.1 were mapped to this region in Trial 1 of this study. This region harbored QTLs for RPI, BRT, thickness and dry weight of penetrated roots under simulated soil hardpans (Zhang et al., 2001), deep roots per tiller and root thickness in a greenhouse study (Kamoshita et al., 2002), and RPF and number of panicles under rainfed condition in the field in Thailand (Zhang et al., 1999b). This region was earlier found to regulate root thickness in two other rice populations viz., IR64/ Azucena DH lines (Zheng et al., 2000) and CO39/Moroberekan RI lines (Champoux et al., 1995). Further, RG214 was one among the best three-marker model explaining variation in drought avoidance in the field (Champoux et al., 1995), and was also linked to total root number (Ray et al., 1996) in C039/Moroberekan RI lines. In these populations, the favorable alleles for desirable root system traits came from japonica parents. CT9993, the japonica accession contributed the favorable alleles for plant growth and production traits under stress in this study and also for the root related drought resistance traits (Zhang et al., 2001) in this population. This genomic region may contain either one gene derived from CT9993 having pleiotropic effects on root traits or more genes conferring several different root traits with a positive effect on plant production under drought in the field. This genomic region is identified repeatedly as being linked to drought avoidance via deep and thick root system in rice across several genetic backgrounds and environments.
The QTLs rwc9.1 and dhs9.1 on chromosome 9 identified in the present study mapped to the same location as QTLs for grain yield, panicle number, plant height, and days to 50% flowering under rainfed conditions in an experiment conducted in Thailand by means of this DH population (Zhang et al., 1999b). This region was found to be closer to QTLs for penetrated root thickness, ME9_6-K985 in this population (Zhang et al., 2001) and Amy3ABC-RZ12 in IR64/Azucena DH lines (Zheng et al., 2000) under simulated soil hardpans. In this same region, QTLs (RZ12-RG570) for root thickness and root/shoot dry weight ratio were identified in CO39/Moroberekan RI lines (Champoux et al., 1995). QTLs for several root morphological traits and leaf rolling under drought stress in IR64/Azucena DH lines were also found to overlap at this region (Yadav et al., 1997). This QTL region was consistently linked to leaf rolling in two different populations of rice, viz., CO39/Moroberekan RI lines (Champoux et al., 1995) and IR64/Azucena DH lines (Courtois et al., 2000).
There are several such instances wherein some of the QTLs for rice plant performance under stress in this study mapped to the same location as QTLs for different root traits in this population, rwc1.1 on chromosome 1 identified in this study mapped to the same region linked to deep root mass, deep root ratio, and deep roots per tiller in this population (Kamoshita et al., 2002). On chromosome 2, ry2.1 overlapped with QTLs for shoot biomass, root depth, and root thickness in these DH lines (Kamoshita et al., 2002). Price et al. (2002) have reported poor colocation of drought avoidance and root trait QTLs in rice. The results of the present study on the other hand showed considerable amount of colocation of drought avoidance and root trait QTLs in these DH lines. More than 50% of the QTLs for field drought avoidance overlapped with QTLs for root morphology in Co39/Moroberekan RI population and all the alleles that had positive effect in either case were derived from Moroberekan, the japonica parent (McCouch and Doerge, 1995).
Comparison of QTLs for OA and Rice Performance under Drought Stress
The QTLs ld8.1 and dhc8.1 on chromosome 8 identified in the present study were located in the same genomic region as a QTL for OA (Zhang et al., 2001), canopy temperature, and days to heading under drought at Bet Dagan, Israel, and days to 50% flowering under rainfed conditions in Thailand (Zhang et al., 1999b) in this population. QTLs lr8.1 and his8.1 on chromosome 8 identified in the present study mapped to the same region as the OA QTL detected in a recombinant inbred (RI) line population of rice (Lilley et al., 1996). OA maintains positive turgor in water-stressed plants allowing better panicle exsertion which enables higher spikelet fertility, harvest index, and yield stability in crop plants (Ludlow and Muchow, 1990). However, no QTL for grain yield under stress was mapped to this chromosomal region in this study.
The QTL bras3.1 on chromosome 3 was detected for biomass under mild stress in Trial 2 of the present study. A major QTL for OA was earlier located at the same region in these DH lines (Zhang et al., 2001) and the positive alleles for both biomass and OA were contributed by the indica parent, IR62266. Two RFLP markers flanking this QTL are RZ313 and RG369. In this region, a QTL for stomatal behavior was detected in a rice [F.sub.2] mapping population (Price et al., 1997). These results suggest that genes in this genomic region might have been conserved to respond to drought.
Phenotypic Correlations between Secondary Traits and Field Performance
Results thus indicated overlapping of some of the QTLs for plant water stress indicators and production traits under drought stress identified in this study and QTLs for several root traits reported earlier (Zhang et al., 1999b; Zhang et al., 2001; Kamoshita et al., 2002) in these 154 DH lines. Champoux et al. (1995) found that 12 out of 14 QTLs for drought avoidance in the field overlapped with QTLs for root morphology in rice. Courtois et al. (2000) observed that some of the QTLs for relative growth rate of rice under water stress in the field conditions were mapped to the same regions as QTLs for root morphology. The same location of QTLs for different traits should be associated with a correlation of the phenotypic data, if there is pleiotropy or genetic linkage among the traits (Paterson et al., 1991). Phenotypic correlations were estimated between plant water stress indicators, phenology, and production traits under drought stress from the three trials of this study and the drought resistance component traits (root traits and OA capacity) of these 154 DH lines (as determined by Zhang et al., 1999b, 2001; Kamoshita et al., 2002). Plant water stress indicators, phenology, and production traits under stress in this study were positively correlated with several root traits and the correlation coefficients for Trial 1 are presented (Table 5), as the water stress was more severe and also data for grain yield was available in this trial. While, grain yield in control was not correlated with most traits (data not shown), except BRT (r = [0.36.sup.**]), grain yield under drought was positively correlated with several root traits. Plant biomass, grain yield and number of grains per panicle under stress in trial 1 were positively correlated with BRT (r = [0.32.sup.**], [0.34.sup.**], and [0.36.sup.**], respectively). Further, the correlations between several root traits with grain yield, biomass, and number of grains per panicle were higher under drought stress than in control (data not shown). While 1000-grain weight in control was not correlated to any of the root traits, 1000-grain weight under stress was positively correlated with deep root dry weight (r = [0.32.sup.**]) and other root traits in this trial. Harvest index under stress was positively correlated with several of the root traits in these DH lines. Root morphology and rooting patterns directly affect the amount of water available to a crop, and increased width, depth and branching of root system have been shown to decrease plant water stress in rice (O'Toole and Soemartono, 1981). The ability of rice to penetrate compacted soil is linked with the capacity to develop thick and long root axes (Yu et al., 1995) and this has been shown to contribute to drought resistance. A root system that extends the root zone to more fully exploit available soil water has the potential to increase yield under drought (Mambani and Lal, 1983).
Since the stress was relatively mild in Trial 2, yield and yield components under stress were not significantly correlated with any of the drought resistance component traits. Under mild stress conditions (yield loss less than 50%, as in this trial), yield is determined more by yield potential and phenotype than by drought resistance mechanisms per se and no significant relationship between drought resistance component traits and grain yield is expected in rice (Pantuwan et al., 2002).
In Trial 3, where stress was very severe, plant biomass under stress (the only measure of plant production in this trial) was positively correlated with BRT (r = [0.37.sup.**]). Biomass under stress in turn had significant negative correlation with canopy temperature (r = [-0.69.sup.**]) measured in a subset of 40 DH lines in this trial. Canopy temperature on the other hand showed significant negative correlation with BRT (r = [-0.44.sup.**]). Differences in canopy temperature among rice cultivars are known to be related to drought avoidance based mainly on the potential to maintain transpiration under stress, and canopy temperature was shown to be negatively correlated with biomass and grain yield under water-deficit in rice (Garrity and O'Toole, 1995; Blum et al., 1999). Root architecture greatly affects water balance of the plant and therefore yield reduction under stress. Champoux et al. (1995) earlier reported good correlation between root thickness and field drought avoidance in rice. Plants with a deeper root system would maintain a cooler canopy temperature and ultimately higher yield under drought as seen in maize (Sanguineti et al., 1999). However, in a study by Pantuwan et al. (2002), root pulling resistance was not correlated with grain yield in rice genotypes under drought in rainfed lowland conditions. Deep root systems would be normally beneficial in upland conditions, as in this study, where drought stress is common in the continuously aerobic soils. Further, the importance of phenotyping environment and the prospects for selection of QTLs for deep root morphology and root thickness under anaerobic conditions to improve constitutive root system of rainfed lowland rice has been demonstrated (Kamoshita et al., 2002). Thus, the finding of the present study indicated the scope for drought resistance improvement in rice through selection for root traits by means of MAS.
Yield and plant production traits under stress were not correlated with the capacity for OA (as determined by Zhang et al., 2001) in these 154 DH lines in the three trials (data not shown). Thus, capacity for OA did not impact plant production under stress in the two sites of this study. Similar results were reported earlier in rice (Zhang et al., 1999b). One of the possible reasons for lack of correlation between OA capacity and field performance in the present experiments may be that the OA in these DH lines was determined in a separate experiment, where all the lines were subjected to the same leaf RWC by growing them in pots (Zhang et al., 2001). This was done because leaf water status affects the rate of OA. In the present field experiments, leaf water status was not the same in all the DH lines (because of their difference in root traits and also due to the unlimited vertical soil volume inherent to field conditions) and hence the differences in OA capacity among the lines could not be fully expressed.
In summary, overlap between QTLs for plant production under stress and QTLs for different root traits was observed in these rice DH lines. This suggests the presence of genes with pleiotropic effects on the investigated traits. The QTLs identified on chromosome 4 for root-related drought resistance traits also had pleiotropic effect on yield traits under drought stress in the field. When QTLs for different traits overlapped, the favorable alleles came from the same parent for the traits. The positive association between QTLs for root traits and rice yield under drought in the field might be useful in MAS for rainfed rice improvement.
Abbreviations: AFLP, amplified fragment length polymorphism; BRT, basal root thickness; DAS, days after sowing; DH, doubled haploid; MAS, marker-assisted selection; OA, osmotic adjustment; QTLs, quantitative trait loci; RFLP, restriction fragment length polymorphism; RPF, root pulling force; RPI, root penetration index; RWC, relative water content; SSR, simple sequence repeat.
Table 1. Site, soil and drought stress characterization for trial 1 (1999 wet season), trial 2 (1999-2000 wet season), and trial 3 (2000 dry season) conducted in two locations in India. Trial 1 Trial 2 Site Coimbatore Paramakudi Elevation (meters above sea 427 40 level) Latitude and longitude 11[degrees]N, 9[degrees]N, 77[degrees]E 70[degrees]E Soil texture Clay loam Sandy clay Soil pH 8.4 8.1 Characterization of the stress Severe Mild Timing of start of stress 63 DAS ([dagger]) 85 DAS Duration of stress period (d) 25 12 Rainfall during stress period 0 0 (mm) Rainfall during crop period 332 643 (mm) Number of rainy days 25 52 Mean temperature ([degrees]C)- maximum 32.0 33.0 minimum 20.1 20.2 Average relative humidity (%) 72.3 89.7 Trial 3 Site Coimbatore Elevation (meters above sea 427 level) Latitude and longitude 11[degrees]N, 77[degrees]E Soil texture Clay loam Soil pH 8.4 Characterization of the stress Very severe Timing of start of stress 83 DAS Duration of stress period (d) 33 Rainfall during stress period 0 (mm) Rainfall during crop period 58 (mm) Number of rainy days 9 Mean temperature ([degrees]C)- maximum 33.4 minimum 22.3 Average relative humidity (%) 63.9 ([dagger]) DAS = days after sowing. Table 2. Trait mean values for CT9993, IR62266, and 154 rice doubled-haploid (DH) lines for three trials. Trait Trial CT9993 IR62266 Relative water content (%) Trial 1 94.0 69.3 Trial 3 80.3 67.6 Canopy temperature ([degrees]C) Trial 3 31.2 34.2 Leaf rolling ([double dagger]) Trial 1 5.0 6.7 Trial 2 1.0 3.0 Trial 3 1.7 6.3 Leaf drying ([double dagger]) Trial 1 4.0 5.7 Trial 3 1.3 5.3 Days to heading (DAS) Trial 1 110 121 ([section])--stress Trial 2 93 93 Trial 3 111 >130 Days to heading (DAS)--control Trial 1 98 106 Trial 2 NA ([dagger NA dagger]) Trial 3 106 110 Plant height (cm)--stress Trial 1 76.1 47.7 Trial 2 74.2 61 Plant height (cm)--control Trial 1 75.9 51.9 Trial 2 86.8 71.8 Grain yield (g [m.sup.-2])--stress Trial 1 21.7 11.1 Trial 2 56.3 175 Grain yield (g [m.sup.-2])--control Trial 1 120.0 71.2 Trial 2 NA 398 Biomass (g [m.sup.-2])--stress Trial 1 285.6 212.2 Trial 2 356 873 Trial 3 265.3 71.7 Biomass (g [m.sup.-2])--control Trial 1 605.8 335.1 Trial 2 NA 1148 Trial 3 460.0 119.2 Spikelet fertility (%)--stress Trial 1 68.2 55.3 Trial 2 74.9 65.7 Spikelet fertility (%)--control Trial 1 82.1 73.8 Trial 2 72.3 79.4 Grains per panicle--stress Trial 1 52.0 25.0 Grains per panicle--control Trial 1 87.7 35.3 1000-grain weight (g)--stress Trial 1 20.6 17.9 1000-grain weight (g)--control Trial 1 21.7 30.2 Harvest index (%)--stress Trial 1 7.59 5.70 Harvest index (%)--control Trial 1 20.6 21.2 Relative biomass Trial 1 47.1 63.3 Trial 2 NA 76.1 Trial 3 57.7 60.1 Relative yield Trial 1 18.1 15.6 Trial 2 NA 44.1 DH lines Trait Trial Mean Range Relative water content (%) Trial 1 68.3 32.8-95.0 Trial 3 55.1 22.9-87.4 Canopy temperature ([degrees]C) Trial 3 33.8 31.2-36.1 Leaf rolling ([double dagger]) Trial 1 5.7 2.7-7.0 Trial 2 2.0 1.0-5.4 Trial 3 4.6 1.0-7.0 Leaf drying ([double dagger]) Trial 1 4.8 1.3-7.0 Trial 3 3.7 1.0-7.0 Days to heading (DAS) Trial 1 105 77-122 ([section])--stress Trial 2 87 77-96 Trial 3 105 89-128 Days to heading (DAS)--control Trial 1 91 74-106 Trial 2 79 69-90 Trial 3 99 85-117 Plant height (cm)--stress Trial 1 61.8 34.1-88.1 Trial 2 71.7 38.1-100.2 Plant height (cm)--control Trial 1 65.6 38.2-88.2 Trial 2 81.9 44.9-119.5 Grain yield (g [m.sup.-2])--stress Trial 1 37.3 6.9-108 Trial 2 121.6 50-281 Grain yield (g [m.sup.-2])--control Trial 1 115.5 26-235 Trial 2 230.5 88-508 Biomass (g [m.sup.-2])--stress Trial 1 252.6 132-469 Trial 2 460.6 207-873 Trial 3 153.4 54-265 Biomass (g [m.sup.-2])--control Trial 1 484.3 221-883 Trial 2 843.3 112-1633 Trial 3 403.8 117-854 Spikelet fertility (%)--stress Trial 1 64.7 13.3-85.7 Trial 2 72.1 43.6-91.0 Spikelet fertility (%)--control Trial 1 77.6 37.6-92.6 Trial 2 76.1 48.3-94.6 Grains per panicle--stress Trial 1 45.4 6.3-83.3 Grains per panicle--control Trial 1 59.9 25.3-122.7 1000-grain weight (g)--stress Trial 1 21.0 14.9-25.1 1000-grain weight (g)--control Trial 1 22.4 16.3-30.2 Harvest index (%)--stress Trial 1 13.7 4.0-28.9 Harvest index (%)--control Trial 1 23.9 9.2-47.7 Relative biomass Trial 1 54.7 24.5-95.3 Trial 2 56.9 20.8-97.8 Trial 3 40.7 12.8-76.3 Relative yield Trial 1 33.5 9.2-89.8 Trial 2 53.0 20.5-93.5 SD Trait Trial ([dagger]) H Relative water content (%) Trial 1 12.5 0.49 Trial 3 15.2 0.73 Canopy temperature ([degrees]C) Trial 3 1.2 0.86 Leaf rolling ([double dagger]) Trial 1 1.0 0.65 Trial 2 1.5 0.78 Trial 3 1.2 0.70 Leaf drying ([double dagger]) Trial 1 1.1 0.71 Trial 3 1.1 0.70 Days to heading (DAS) Trial 1 7.7 0.70 ([section])--stress Trial 2 3.8 0.96 Trial 3 NS ([para- -- (#) graph]) Days to heading (DAS)--control Trial 1 6.0 0.93 Trial 2 4.4 0.96 Trial 3 7.0 0.75 Plant height (cm)--stress Trial 1 11.5 0.49 Trial 2 13.9 0.93 Plant height (cm)--control Trial 1 11.2 0.79 Trial 2 15.4 0.54 Grain yield (g [m.sup.-2])--stress Trial 1 21.0 0.59 Trial 2 49.7 0.81 Grain yield (g [m.sup.-2])--control Trial 1 43.2 0.61 Trial 2 85.5 0.84 Biomass (g [m.sup.-2])--stress Trial 1 65.0 0.37 Trial 2 154.9 0.84 Trial 3 40.6 0.60 Biomass (g [m.sup.-2])--control Trial 1 139.5 0.60 Trial 2 341.3 0.78 Trial 3 147.8 0.71 Spikelet fertility (%)--stress Trial 1 10.7 0.43 Trial 2 NS -- Spikelet fertility (%)--control Trial 1 9.1 0.69 Trial 2 11.1 0.65 Grains per panicle--stress Trial 1 12.9 0.50 Grains per panicle--control Trial 1 17.7 0.76 1000-grain weight (g)--stress Trial 1 2.0 0.37 1000-grain weight (g)--control Trial 1 2.5 0.66 Harvest index (%)--stress Trial 1 5.4 0.60 Harvest index (%)--control Trial 1 6.2 0.24 Relative biomass Trial 1 15.1 -- Trial 2 18.7 -- Trial 3 13.4 -- Relative yield Trial 1 16.0 -- Trial 2 19.7 -- ([dagger]) SD = standard deviation. ([double dagger]) Based on 1 to 7 scale. ([section]) DAS = days after sowing. ([paragraph]) NS = not significant. ([sharp]) -- = derived data. ([dagger dagger]) NA = data not available. Table 3. Correlation coefficients among leaf relative water content (RWC), leaf rolling (LR), leaf drying (LD), days to heading (DH), biomass (BM), grain yield (GY), spikelet fertility (SF), number of grains per panicle (GPP), 1000-grain weight (TGWt), and harvest index (HI) under drought stress in the field in trial 1, (Coimbatore, India 1999 wet season) in a DH line population of rice. RWC LR LD DH BM RWC 1.00 -0.26 ** -0.22 ** -0.26 ** -0.00 LR 1.00 0.92 ** 0.25 ** -0.18 * LD 1.00 0.27 ** -0.17 * DH 1.00 0.04 BM 1.00 GY SF GPP TGWt HI GY SF GPP TGWt HI RWC -0.00 -0.17 * 0.05 0.07 0.04 LR -0.24 ** -0.14 0.01 -0.15 -0.26 ** LD -0.29 ** -0.17 * 0.03 -0.18 * -0.33 ** DH -0.02 -0.16 -0.10 -0.18 * -0.06 BM 0.74 ** 0.35 ** 0.45 ** 0.25 ** 0.40 ** GY 1.00 0.46 ** 0.45 ** 0.31 ** 0.88 ** SF 1.00 0.50 ** 0.28 ** 0.44 ** GPP 1.00 0.36 ** 0.39 ** TGWt 1.00 0.30 ** HI 1.00 * Significant at 0.05 probability level. ** Significant at 0.01 probability level. Table 4. QTLs detected with LO[D.sup.3] [greater than or equal to] 2.85 for relative water content (%), leaf temperature ([degrees]C), leaf rolling and drying scores, days to heading (days after sowing), plant height (cm), grain yield (g [m.sup.-2]), biomass (g [m.sup.-2]), spikelet fertility (%), grains per panicle, harvest index (%), and relative yield (%) by interval mapping via QTLMapper version 1.0 in a doubled-haploid rice population of 154 lines from CT9993 and IR62266 from different trials. Individual QTL are designated with the italicized abbreviation of the trait and chromosome number. When more than one QTL affects a trait on the same chromosome, they are distinguished by decimal numbers. Chromosome Traits Trial QTL number Relative water Trial 1 rwc1.1 1 content Trial 3 rwc9.1 9 ([section]) Canopy temperature Trial 3 ct2.1 2 ([section]) Leaf rolling Trial 1 lr1.1 1 lr8.1 8 lr11.1 11 Leaf drying Trial 1 ld1.1 1 ld6.1 6 ld8.1 8 Days to heading-- Trial 1 dhs3.1 3 stress dhs8.1 8 Trial 2 dhs9.1 9 Days to heading-- Trial 1 dhc3.1 3 control dhc3.2 3 Trial 2 dhc9.1 9 Trial 3 dhc8.1 8 Plant height-- Trial 1 phs1.1 1 stress phs2.1 2 phs4.1 4 phs7.1 7 Trial 2 phs1.1 1 Plant height-- Trial 1 phc1.1 1 control phc4.1 4 Trial 2 phc1.1 1 phc2.1 2 phc7.1 7 Grain yield-- Trial 1 gys1.1 1 stress gys4.1 4 Trial 2 gys1.1 1 gys10.1 10 gysl2.1 12 Biomass--stress Trial 1 bms12.1 12 Trial 2 bms3.1 3 Biomass--control Trial 1 bmc2.1 2 bmc2.2 2 bmc4.1 4 bmc9.1 9 Trial 2 bmc3.1 3 bmc8.1 8 Grains per Trial 1 gpps4.1 4 panicle--stress Grains per Trial 1 gppc1.1 1 panicle--control gppc3.1 3 Harvest index-- Trial 1 his8.1 8 stress Harvest index-- Trial 1 hic1.1 1 control hic11.1 11 Relative yield Trial 2 ry2.1 2 ry11.1 11 Traits Trial QTL Interval Relative water Trial 1 rwc1.1 RM212-C813 content Trial 3 rwc9.1 RM215-RG667 ([section]) Canopy temperature Trial 3 ct2.1 ME9_7-K706 ([section]) Leaf rolling Trial 1 lr1.1 RG109-ME10_14 lr8.1 ME6_13-G187 lr11.1 ME10_16-ME6_2 Leaf drying Trial 1 ld1.1 RG109-ME10_14 ld6.1 ME14_9-RZ682 ld8.1 G2132-G1073 Days to heading-- Trial 1 dhs3.1 R2170-RZ672 stress dhs8.1 ME9_1-ME2_1 Trial 2 dhs9.1 RG667-RM201 Days to heading-- Trial 1 dhc3.1 RG104-EM11_9 control dhc3.2 R2170-RZ672 Trial 2 dhc9.1 RM215-RG667 Trial 3 dhc8.1 G2132-G1073 Plant height-- Trial 1 phs1.1 RG109-ME10_14 stress phs2.1 ME2_7-EMP2_7 phs4.1 RG476-RG214 phs7.1 RG528-RG769 Trial 2 phs1.1 RG109-ME10_14 Plant height-- Trial 1 phc1.1 EM11_11-RG109 control phc4.1 RG476-RG214 Trial 2 phc1.1 RG109-ME10_14 phc2.1 RG437-ME10_18 phc7.1 RG404-CDO38 Grain yield-- Trial 1 gys1.1 EM18_10-L1087 stress gys4.1 RG476-RG939 Trial 2 gys1.1 RG811-ME2_16 gys10.1 EMP2_9-ME5_16 gysl2.1 EM14_2-EM19_5 Biomass--stress Trial 1 bms12.1 ME6_12-G2140 Trial 2 bms3.1 RZ313-EM17_1 Biomass--control Trial 1 bmc2.1 ME2_7-EMP2_7 bmc2.2 EM14_4-RZ386 bmc4.1 RG620-C107 bmc9.1 ME9_3-ME9_6 Trial 2 bmc3.1 RG369-EM19_4 bmc8.1 ME2_1-EM16_6 Grains per Trial 1 gpps4.1 RG214-RG620 panicle--stress Grains per Trial 1 gppc1.1 EM11_11-RG109 panicle--control gppc3.1 RG104-EM11_9 Harvest index-- Trial 1 his8.1 G187-ME2_ll stress Harvest index-- Trial 1 hic1.1 RZ543-RG1028 control hic11.1 G257-ME2_6 Relative yield Trial 2 ry2.1 EM11_10-EM18_13 ry11.1 EM18_8-G320 Traits Trial QTL LOD Relative water Trial 1 rwc1.1 3.59 content Trial 3 rwc9.1 8.59 ([section]) Canopy temperature Trial 3 ct2.1 4.24 ([section]) Leaf rolling Trial 1 lr1.1 6.56 lr8.1 4.43 lr11.1 6.78 Leaf drying Trial 1 ld1.1 7.64 ld6.1 3.90 ld8.1 4.77 Days to heading-- Trial 1 dhs3.1 4.41 stress dhs8.1 3.73 Trial 2 dhs9.1 4.48 Days to heading-- Trial 1 dhc3.1 6.73 control dhc3.2 7.95 Trial 2 dhc9.1 3.60 Trial 3 dhc8.1 6.10 Plant height-- Trial 1 phs1.1 14.27 stress phs2.1 4.01 phs4.1 7.82 phs7.1 3.53 Trial 2 phs1.1 15.84 Plant height-- Trial 1 phc1.1 22.79 control phc4.1 8.97 Trial 2 phc1.1 25.09 phc2.1 7.31 phc7.1 7.96 Grain yield-- Trial 1 gys1.1 4.41 stress gys4.1 4.75 Trial 2 gys1.1 4.16 gys10.1 4.41 gysl2.1 7.50 Biomass--stress Trial 1 bms12.1 5.17 Trial 2 bms3.1 4.43 Biomass--control Trial 1 bmc2.1 10.19 bmc2.2 6.42 bmc4.1 6.75 bmc9.1 6.86 Trial 2 bmc3.1 4.77 bmc8.1 3.17 Grains per Trial 1 gpps4.1 3.58 panicle--stress Grains per Trial 1 gppc1.1 5.69 panicle--control gppc3.1 5.07 Harvest index-- Trial 1 his8.1 5.30 stress Harvest index-- Trial 1 hic1.1 3.25 control hic11.1 3.25 Relative yield Trial 2 ry2.1 4.26 ry11.1 2.94 [R.sup.2] Effect ([double Traits Trial QTL ([dagger]) dagger]) Relative water Trial 1 rwc1.1 4.24(C) 12.1 content Trial 3 rwc9.1 7.73(C) 58.8 ([section]) Canopy temperature Trial 3 ct2.1 0.86(C) 47.1 ([section]) Leaf rolling Trial 1 lr1.1 0.37(C) 15.2 lr8.1 0.30(I) 10.1 lr11.1 0.40(I) 16.5 Combined 53.1 effect Leaf drying Trial 1 ld1.1 0.65(C) 20.8 ld6.1 0.28(I) 8.5 ld8.1 0.34(I) 12.5 Combined 41.8 effect Days to heading-- Trial 1 dhs3.1 2.38(I) 9.3 stress dhs8.1 2.33(I) 8.9 Combined 27.9 effect Trial 2 dhs9.1 1.80(I) 23.8 Days to heading-- Trial 1 dhc3.1 2.48(I) 27.0 control dhc3.2 2.34(I) 24.1 Combined 36.1 effect Trial 2 dhc9.1 1.98(I) 19.5 Trial 3 dhc8.1 2.96(I) 18.1 Plant height-- Trial 1 phs1.1 6.82(C) 27.8 stress phs2.1 3.08(C) 4.8 phs4.1 4.91(C) 14.4 phs7.1 3.30(I) 6.5 Combined 59.1 effect Trial 2 phs1.1 10.93(C) 46.8 Plant height-- Trial 1 phc1.1 6.64(C) 45.8 control phc4.1 3.92(C) 16.0 Combined 64.4 effect Trial 2 phc1.1 11.42(C) 46.5 phc2.1 5.38(C) 10.3 phc7.1 5.97(I) 12.7 Combined 72.6 effect Grain yield-- Trial 1 gys1.1 7.91(I) 11.7 stress gys4.1 9.19(C) 15.8 Combined 33.9 effect Trial 2 gys1.1 10.65(I) 7.8 gys10.1 12.84(I) 11.4 gysl2.1 18.00(C) 22.3 Combined 53.9 effect Biomass--stress Trial 1 bms12.1 27.25(C) 17.2 Trial 2 bms3.1 53.34(I) 15.5 Biomass--control Trial 1 bmc2.1 65.63(C) 22.2 bmc2.2 58.90(I) 17.9 bmc4.1 54.69(C) 15.4 bmc9.1 52.69(C) 14.3 Combined 53.7 effect Trial 2 bmc3.1 146.46(I) 19.3 bmc8.1 102.69(C) 9.5 Combined 28.7 effect Grains per Trial 1 gpps4.1 4.09(C) 13.6 panicle--stress Grains per Trial 1 gppc1.1 6.79(C) 15.2 panicle--control gppc3.1 8.00(C) 21.1 Combined 36.3 effect Harvest index-- Trial 1 his8.1 1.82(I) 14.7 stress Harvest index-- Trial 1 hic1.1 2.11(I) 10.8 control hic11.1 2.32(C) 13.0 Combined 23.8 effect Relative yield Trial 2 ry2.1 ll.10(C) 22.0 ry11.1 7.25(I) 9.4 Combined 41.3 effect ([dagger]) Letters I and C in parenthesis indicate that positive or favorable alleles for the effects are from IR62266 and CT9993, respectively. ([double dagger]) Relative contributions of the detected putative QTLs to the phenotypic variation. ([section]) QTL for RWC and canopy temperature were mapped using data from 40 DH lines in Trial 3. Table 5. Correlation coefficients among leaf relative water content (RWC), leaf rolling (LR), leaf drying (LD), days to heading (DH), biomass (BM), grain yield (GY), spikelet fertility (SF), number of grains per panicle (GPP), 1000-grain weight (TGWt) and harvest index (HI) under drought stress in the field in trial 1, (Coimbatore, India 1999 wet season) and osmotic adjustment (OA), root penetration index (RPI), basal root thickness (BRT), root pulling force (RPF), deep root thickness (DPRT), deep root dry weight (DRDW) and root depth (RDEPTH) in a DH line population of rice. Traits OA RPI BRT RPF RWC 0.19 * 0.00 0.11 -0.09 LR -0.02 -0.11 -0.01 0.06 LD 0.02 -0.06 0.00 0.06 DH 0.27 ** 0.14 -0.18 * 0.30 ** BM -0.01 0.23 ** 0.32 0.17 * GY -0.03 0.18 * 0.34 ** 0.17 * SF -0.06 0.20 * 0.02 -0.01 GPP 0.05 0.19 * 0.36 ** 0.23 ** TGWt 0.11 -0.05 0.22 ** 0.06 HI 0.04 0.12 0.24 ** 0.16 * Traits DPRT DRDW RDEPTH RWC -0.11 -0.10 -0.02 LR -0.21 * 0.04 0.01 LD 0.22 * 0.02 -0.04 DH -0.15 0.08 -0.12 BM 0.31 ** 0.28 ** 0.23 ** GY 0.28 ** 0.22 * 0.25 ** SF 0.08 0.12 0.14 GPP 0.03 0.13 0.06 TGWt 0.24 ** 0.32 ** 0.28 ** HI 0.20 * 0.16 0.26 ** * Significant at 0.05 probability level. ** Significant at 0.01 probability level.
This research was supported by the Rockefeller Foundation, USA. Thanks to Drs. A. Blum, J. C. O'Toole and J.M. Lilley for helpful comments on the manuscript. This is a contribution number T-4-501 of the College of Agricultural Sciences and Natural Resources, Texas Tech University, Lubbock, USA. Declaration: The experiments comply with the current laws of the country in which the experiments were performed.
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R. Chandra Babu, * Bay D. Nguyen, Varapong Chamarerk, P. Shanmugasundaram, P. Chezhian, P. Jeyaprakash, S. K. Ganesh, A. Palchamy, S. Sadasivam, S. Sarkarung, L. J. Wade, and Henry T. Nguyen
R. Chandra Babu, P. Shanmugasundaram, P. Chezhian, S. K. Ganesh and S. Sadasivam, Center for Plant Molecular Biology, Tamil Nadu Agrl. University, Coimbatore-641 003, India; B. D. Nguyen and Varapong Chamarerk, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA; P. Jeyaprakash and A. Palchamy, Agricultural Research Station, Paramakudi, India; S. Sarkarung, International Rice Research Institute, Chatuchak, Bangkok 10900, Thailand; L.J. Wade, University of Western Australia, 35, Stirling Highway, Crawley, WA 6009; H.T. Nguyen, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA. Received 17 April 2002. * Corresponding author (firstname.lastname@example.org).
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|Author:||Babu, R. Chandra; Nguyen, Bay D.; Chamarerk, Varapong; Shanmugasundaram, P.; Chezhian, P.; Jeyapraka|
|Date:||Jul 1, 2003|
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