2.1. Experimental design
The plants used in this experiment were 1-year sassafras seedlings, which are common native tree species in Sichuan. The seedlings were obtained from the same source and were pests and diseases free. The tested soil was a yellow soil taken from a forest farm in Ya’an city, Sichuan Province. The pH of the test soil was 5.75, and the total nitrogen, phosphorus and potassium contents were 9.10 g/kg, 0.64 g/kg, and 12.59 g/kg, respectively.
The experiment was conducted at the teaching and research station of Sichuan Agricultural University. Its geographical coordinates are east longitude 103°51¢29² and north latitude 30°42¢18². Approximately 12 kg of mixed soil was weighed and placed into each pot. The content of Cd2+ (mg/kg) in the soil of each pot was calculated according to the dry weight of the soil, and Cd2+ was added to the pots in the form of a CdCl2 aqueous solution. A single-factor test design was used to establish 5 cadmium treatment levels: CT (0 mg/kg), Cd5 (5 mg/kg), Cd20 (20 mg/kg), Cd50 (50 mg/kg), and Cd100 (100 mg/kg). Five replicates were established for each treatment group, and all treated plants were placed in a greenhouse.
The experiment began in early May 2019. CdCl2 was applied 5 times, with an interval of 15 d; the same amount of pure water was added to CT. At the time of application, the prepared CdCl2 solution was poured evenly onto the soil surface in the basin. Any CdCl2 solution that exuded from the pot was collected at the bottom of the basin with a tray pad and poured back into the soil. The CdCl2 applications ended in mid-July 2019. After 30 d of plant growth, the plant photosynthetic and physiological characteristics were measured. The plants were harvested at the end of December 2019, and the plant roots, branches and leaves were harvested separately.
2.2. Determination of the plant growth index
The height and ground diameter of each plant were measured before and after the experiment, and the subtraction method was used to calculate the results. The height of the seedlings was measured with a ruler (precision: 0.1 cm). An electronic Vernier caliper (precision: 0.1 mm) was used to measure the ground diameter from two perpendicular directions at the root neck, and the average value was calculated. The plant samples were washed with deionized water, and then the plant organs, i.e., roots, branches and leaves, were harvested separately. In the lab, the plant organs were put in an oven at 105 °C for 30 min, after which they were dried at 70 °C to constant weight and weighed. The biomass of each dried organ was then calculated.
2.3. Determination of the physiological characteristics
In the 5 replicates of each treatment, five mature fresh functional leaves were randomly selected from the middle to upper part of the tree canopy and placed separately into an ice box for the measurement of each index.
The content of free proline (Pro) was extracted with sulfonyl salicylic acid and determined with acidic ninhydrin colorimetry [19]. The content of soluble protein (SP) was determined by the Coomassie brilliant blue method [20]. The malondialdehyde (MDA) and soluble sugar contents (SS) were determined by the thiobarbituric acid heating colorimetric method [20]. The activity of superoxide dismutase (SOD) was determined by the hydroxylamine method [21]. Catalase (CAT) activity was determined by the ammonium molybdate method [22]. Peroxidase (POD) activity was determined by guaiacol spectrophotometry [20]. The H2O2 content was determined with a method based on H2O2 and titanium ions forming a colored [TiO(H2O2)]2+ coordination compound (the specific absorption peak is 410 nm) [23]. All enzyme activities were determined with kits produced by Nanjing Jiancheng Biological Research Company.
2.4. Determination of the chlorophyll content
Two to three mature functional leaves of the plants in each treatment were randomly selected for the determination of chlorophyll content. During the determination, after removing the veins of the leaf, cut the remaining parts into pieces, and store the samples in refrigerator for preservation. An amount of 0.1 g leaves was accurately weighed and placed into a 10 mL centrifuge tube. An amount of 9 ml chlorophyll extraction solution (80% acetone and anhydrous ethanol 1:1 mix) was added, and the mixture was placed in the dark and left for more than 24 h until the leaves were completely white. Spectral measurements were performed at wavelengths of 663 nm and 646 nm.
2.5. Determination of the photosynthetic parameters
The net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and intracellular CO2 concentration (Ci) were determined with an LI-6800 portable photosynthesis system (Li-Cor Inc, USA). Three plants were selected from each treatment, three leaves were selected from each plant, and ten data points were recorded for each leaf.
2.6. Determination of the photosynthetic light-response curve
The LI-6800 portable photosynthesis system (Li-Cor Inc, USA) was used to measure the photosynthetic light-response curves of the leaves selected for the previous determination. The photosynthetically active radiation (PAR) gradient values were 1800, 1600, 1000, 800, 600, 400, 200, 100, 75, 50, 25, and 0 mmol/m2/s, the CO2 concentration was set to 400 ppm, and the room temperature was set to 30°C. Each leaf was photoinduced for 20 min before the determination, and in this test, the photoinduction strength was 800 mmol/m2/s.
The photosynthetic light-response curves of sassafras leaves treated with different concentrations of cadmium were fitted by using Ye’s [25] modified linear hyperbolic model. The fitting equation of the modified linear hyperbolic model is as follows:
![](data:image/png;base64,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)
When Pn=0, the light compensation point (LCP) can be obtained:
![](data:image/png;base64,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)
With dPn/dI=0, the light saturation point (LSP) can be obtained:
![](data:image/png;base64,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)
When I=Is, Pnmax can be obtained:
![](data:image/png;base64,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)
![](data:image/png;base64,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)
2.7. Statistical analysis
All data were collected in Excel 2016 and statistically analyzed with SPSS 24.0. The significance of the differences among means was assessed by one-way analysis of variance (ANOVA). Multiple comparisons were performed mostly with Duncan’s test to compare the mean values between treatments (p<0.05). The figures were drawn in Origin 8.5.