Abstract: Genetic variability among eight rice cultivars was determined by conducting a solution culture experiment using modified Yoshida solution. Three Zn levels were developed by the addition of no DTPA (Zn1), 25 μ M DTPA (Zn2) and 50 μ M DTPA (Zn3) to 0.035 mg L–1 Zn. Substantial differences were observed among genotypes for accumulation of shoot dry weight (SDW), root dry weight (ROW), root: shoot ratio (RSR) as well as for zinc utilization efficiency (ZnUE). Overall Kernel genotype produced maximum SDW at all the three levels of Zn. In case of RDW genotypes Kernel, Kashmir and Basmati-370 produced maximum RDW, while DM-25 was least RDW producer. Highest RSR was exhibited by Kashmir followed by Kernel and Basmati-370. Genotypes Kernel, Basmati-370 and KS-282 showed maximum ZnUE compared to all other genotypes and could be regarded as Zn-efficient.
Introduction
Most soils of Pakistan are alkaline and calcareous with low organic matter content (Khattak and Parveen, 1986). Such soils are usually conducive to nutrient deficiencies particularly of N (nitrogen), P (phosphorus), Zn (zinc), Fe (iron) and/or S (boron). On an average, 49 % of the soils from various regions of the Punjab were observed Zn deficient (NFDC, 1998). During the last few years, a number of high yielding varieties of rice have been evolved and introduced in the cropping systems. This necessitates a proper evaluation of Influence of genetic variability among the new rice varieties on response to plants nutrients. Such basic information could also be used by plant breeders in evolving varieties to suit a soil type. Differential response to Zn fertilization among the new rice genotypes has not been investigated so far. Therefore, the present investigation was undertaken to evaluate the influence of genotype on Zn response in rice.
Materials and Methods
Seeds of eight rice genotypes (NIAB-6, Kernel, Basmati-370, KS-282, IR-6, Basmati-385, Kashmir and DM-25) were obtained from Rice Research Institute, Kale Shah Kaku. Seeds were grown in iron trays lined with polyethylene sheet and containing washed gravels. Canal water was used for irrigation. Nursery was raised prior to transplanting. Fifteen days old seedlings were transplanted in foam-plugged holes in thermopal sheets floating on 200 L modified Yoshida solution (Yoshida et al., 1976) in polyethylene lined iron tubs. The pH of solution was maintained at 5.5 ± 0.5 with HCI or NaOH solutions. Three Zn levels were developed viz. Zn1 (0.035 g L–1 Zn + No DTPA), In2 (0.035 g L–1 Zn + 25 μ M DTPA) and Zn3 (0.035 g L–1 Zn + 50 μ M DTPA). Each genotype consisted of four repeats and each repeat consisted of two plants per hole in the respective tubs. The experiment was laid out in a completely randomized factorial design. Seedlings were harvested after 45 days of transplanting, washed with distilled water, separated into shoot and root and blot dried. The harvested plants were dried at 70̊C for 48 hours. The shoot samples were ground through 40-mesh sieve grinder. The samples were subjected to analysis for growth and physiological parameters. Zinc utilization efficiency was determined as {(1 /nutrient concentration in shoot, mg g–1) x SOW, g 2plant–1} (Siddiqi and Glass, 1981). The data were subjected to statistical analysis using Mstat-C program (Russell and Eisensmith, 1983).
Results and Discussion
Shoot dry weight: Statistical analysis showed significant (p<0.05) differences for shoot dry weight (SOW) on the means of In levels, genotypes and Zn level × genotype interaction (Table 1). Zn1 level produced significantly the highest SOW (7.52) as compared to Zn2 (5.79) and Zn3 (5.35 g 2 plant–1) levels which is in accordance with Cayton et al. (1985).
| Table 1: | Shoot dry weight (g 2 plant–1) of eight rice genotypes at three levels of Zn |
| Values having same letter(s) are statistically non significant at p<0.05. Zn1 = 0.035 g L–1 Zn + No DTPA Zn2 = 0.035 g L–1 Zn + 25 μ DTPA Zn3 = 0.035 g L–1 Zn + 50 μ DTPA | |
They also observed increase in dry matter yield by increasing In levels in the growth medium. It might be due to the fact that with increasing Zn in the growth medium, plants absorb more zinc and translocate it to shoot for dry matter production. Genotype Kernel showed highest mean shoot dry weight (9.31) followed by Basmati-370 (7.49) and KS-282 17.46), whileDM-25 produced minimum (2.47) yield. Similar results were also observed by NFDC, 1998, according to which application of Zn at 2.5-10 kg ha–1 increased the yield of rice genotypes by 10% in Basmati-370 and Basmati-385 and by 12% in genotype 1R-6.
| Table 2: | Root dry weight (g 2 plant–1) of eight rice genotypes at three levels of Zn |
| Values having same letter%) are statistically non significant at p<0.05. | |
| Table 3: | Root-shoot ratio of eight rice genotypes at three levels of Zn |
| Values having same letter%) are statistically non significant at p<0.05. | |
| Table 4: | Zinc utilization efficiency (g2 mg–1) of eight rice genotypes at three levels of Zn |
| Values having same letter%) are statistically non significant at p<0.05. | |
Genotypes showed much differences in the mean SDW at the same level of Zn application in the growth medium. At Zn1 level maximum SDW was produced by Kernel (12.91) followed by Kashmir (10.73), while minimum was produced by DM-25 (2.79). Similarly at Zn2 level maximum shoot dry weight was observed in Kernel (8.22) while minimum was observed in DM-25 (2.37). At Zn3 level maximum SOW was shown by Basmati-370 (7.08) followed by Kernel (6.79) and minimum was shown by DM-25 (2.26). The differences in the means of genotypes at same levels of nutrient in the growth medium show the existence of differential genetic potentials, which can be exploited for the identification of Zn efficient genotypes.
Root dry weight: Data presented in Table 2 shows that differences for RDW were statistically significant (p<0.05) for genotypes and Zn level × genotype interaction. Differences due to Zn levels were non significant, which is contrary to Babiker (1986). He noted significant influence of Zn application on RDW production of different rice cultivars. On an average, Zn2 level showed maximum RDW (0.690) as compared to Zni (0.631) and Zn3 (0.638 g 2 plant–1) levels. The differences in RDW among genotypes were significant. Table 2 shows that Kernel (1.138) produced highest ROW as compared to all other genotypes, while DM-25 produced minimum RDW (0.235). Differences among genotypes at same levels were statistically significant, which indicate wide variation among these genotypes to exploit the same growth environment for production of biomass. DM-25 showed minimum ROW at all the three levels. At Zni level maximum RDW was shown by Kernel (1.350) followed by Kashmir (1.253). At Zn2 and Zn3 levels, highest RDW was observed in Basmati-370. Genotype Kernel showed significant decrease in RDW from Zni (1.350) to Zn2 (1.123) level and non-significant decrease from Zn2 to Zn3 (0.942) level. This might indicate that at Zn stress, the roots of Kernel absorbed Zn from the growth medium and efficiently translocated it to the shoot for dry matter production. Same behavior was also observed in genotype Kashmir.
Root Shoot Ratio (RSR): Statistical analysis showed significant (p<0.05) differences of root: shoot ratio on the means of Zn levels, genotypes and Zn level × genotype interaction (Table 3). The RSR decreased significantly from Zni (0.154) to Zn2 level (0.071) but non-significantly from Zn2 to Zn3 (0.067) level, which is according to Qadir et al. (1988). He also noticed increase in root: shoot ratio with increased Zn application in rice crop. Genotype DM-25 (0.108) showed maximum RSR compared to all other genotypes, while N1AB-6 (0.090) showed minimum value. At Zni level the RSR ranged from 0.131 to 0.174. The lowest RSR was observed in NIAB-6 while DM-25 produced highest RSR. At Zn2 the RSR ranged from 0.064 to 0.076. The lowest ratio was observed in Kernel while DM-25 showed highest ratio. At Zn3 lowest RSR was observed in KS-282 while DM-25 produced highest RSR (0.075). Therefore, it indicates that DM-25 had produced highest RSR at all the levels. Some scientists have recommended higher RSR under nutrient deficient conditions as a suitable screening criterion against nutrient deficiency stress.
Zinc utilization efficiency (ZnUE): Statistical analysis showed significant differences of zinc utilization efficiency (p<0.05) on the means of Zn levels, genotypes and Zn level × genotype interaction (Table 4). The Zn utilization efficiencies increased significantly from Zn, (49.28) to Zn2 (82.73) levels in the growth medium, while remained statistically unchanged from Zn2 to Zn3 (81.43 g2 mg–1) level. High ZnUE may explain adaptability of the genotypes to Zn deficiency stress, as nutrient utilization efficiency of efficient genotypes increase with decreasing nutrient concentration in the growth medium. Maximum ZnUE was shown by genotype Kernel (104.88) followed by KS-282 (91.55), while minimum ZnUE was shown by DM-25 (25.50). Therefore, Kernel was proved most efficient genotype for Zn utilization. DM-25 showed minimum values of ZnUE (15.87, 30.44 and 30.19) at all the threelevels. At Zn1 and Zn2 maximum values of ZnUE were shown by Kernel 182.64 and 127.75), while at Zn3 maximum values were exhibited by 1CS-282 (111.48) followed by Kernel (104.77) and Basmati-370 (103.53). Memon et al. (1989) also documented differences of Zn utilization efficiency in maize crop under differential Zn levels.