The study was undertaken on the Yixing forest farm (31°15′1″-31°15′12″ N, 119°44′2″-119°44′8″ E), located in southern Jiangsu Province, China ( Figure 1 ). The experimental area is within the marine monsoon climate zone in the northern margin of the subtropics, with minimum and maximum temperatures of -4.5°C and 38.8°C, respectively. In addition, the average annual temperature is 16.5°C, and the average monthly temperature is 28.3°C in summer (data from Yixing Forest meteorological station, located 1 km from the field station). Rainfall occurs throughout the year, with an average annual rainfall of 129 days and average annual precipitation of 1229.9 mm, which is concentrated in spring and summer. The terrain is dominated by low hills. The predominant understory species include Oxalis corniculata, Hedyotis chrysotricha, Paederia cruddasiana, and Salvia prionitis (Zheng et al., 2021).

Strip clearcutting refers to cutting all bamboo in the plot. On February 2017, pure Moso bamboo stands with the same management measures, slope, and the same stand structure were selected for the experiment. Three strip cut plots (SC) with a width of 8 m and a length of 20 m were set. Reserved plots (RB) with the same width were set on both sides of the SC. We calculated by weighing the biomass out of the cut and testing the nutrient content, 59384.76 ± 9864.53 kg of biomass out of the strip cut plots. Meanwhile, isolation trenches 50 cm wide and 50 cm deep were dug around the plots to eliminate the effect of physiological integration of moso bamboo through long-distance nutrient transport from adult culms to new bamboo through rhizomes (Su et al., 2019). Three 20 m×20 m traditional management plots were set as the control (CK). The CK plot followed the original management methods, including digging bamboo shoots and artificial selective harvest of old bamboo in on-year, and digging all bamboo shoots in off-year. The SC and RB were restored naturally, and no management measures were taken during the experiment. A continuous survey and sampling was conducted from the three treatment plots from 2017 to 2021. Based on the physiological characteristics of bamboo, age is usually denoted by “du”. A 1-year-old bamboo was labeled as I “du”. Thereafter, bamboo leaves changed every two years, and each time, the age of the bamboo leaves increased by I “du” (Tang et al., 2016). For example, 2− 3-year-old bamboos labeled II “du,” and III “du” represented a 4−year-old bamboo forest (Zheng et al., 2022b). According to the growth characteristics of the Moso bamboo forest, the monitoring period included three On-years (2017, 2019, 2021) and two Off-years (2018, 2020). The basic information of the plot before cutting is shown in Table 1 .

From 2017 to 2021, bamboo shoot dynamics, including the number of shoots (NS), and the number of shoots returned, were investigated every year after the bamboo shoots formed (May). Subsequent to branch drawing and leaf spreading (August), the newly emerging bamboos in the sample plot were investigated, including diameter at breast height (DBH), bamboo height (H), and height to crown base (HCB). We first cut a 13m moso bamboo as a reference, then marked it with paint every 1m, brought it to the sample plot, placed it next to the moso bamboo to be measured, and the same person visually measured HCB and H of the bamboo.

The average DBH, HCB, and H of Moso bamboo at different ages in different treatment plots were calculated. And the Moso bamboo that met the average characteristics of each age of the three treatment plots was selected as the corresponding standard bamboo. In November of each year, according to the statistical data, the standard bamboo was selected to investigate the biomass. To avoid the interference of standard bamboo harvesting on the plots, alternative methods were used to sample biomass of the plots according to the relevant information on the standard bamboo. Twelve standard bamboo were selected from each treatment totaling 36 bamboos. The entire plant is divided into branches, leaves, and culms. Samples were oven-dried at 65°C for 48 h to a constant weight and then weighed. The biomass of each organ of Moso bamboo at stand level was calculated according to Equation 1.

Where i is the age of bamboo, including I, II, and III “du”; j refers to different treatment plots, including SC, RB, and CK; w_ij is the average dry weight of i-year-old standard bamboo branches, culms, and leaves in sample plot j; n_i is the number of i-year-old moso bamboo plants; W_ij is the total dry weight of branches, culms and leaves of i-year-old bamboo in plot j.

According to the biomass of each organ (branch, culm, and leaf) obtained from Moso Bamboo standard bamboo, the biomass of the different organs can be calculated. The specific calculation method is as Equation 2:

Where Δw is the productivity of an organ of Moso bamboo, i is the age of bamboo, n_i is the number of bamboo plants at the age of i, w i ¯ is the biomass of an organ of standard bamboo at the age of i.

From 2017 to 2021, a soil drill was used to stratify soil along the median line (0–40 cm) in each experimental plot in November each year. Ten soil cores were taken from each plot. The mixed samples of soil composition were spread flat on clean paper and placed in a cool and ventilated place indoors to air dry. Stones and undecomposed organic matter were removed, ground, passed through a 2-mm sieve. They were thoroughly mixed, and a part of the sample was placed into a sealed bag for available nutrients determination. The remaining samples were passed through a 2-mm sieve, and were then further ground to pass through a 0.149-mm sieve for determination of soil organic matter (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium content (TK).

The soil organic carbon (SOC) and total nitrogen (TN) content were determined using an elemental analyzer (ECS 4024 CHNSO; Costech, Picarro, Italy). Total phosphorus (TP) content was determined following the molybdenum-antimony resistance colorimetric method (concentrated H2SO4-HClO4) using an automatic chemical analyzer (Smartchem 300; AMS, Italy). Total potassium (TK) content was determined using a flame photometer (M410; Sherwood, United Kingdom). Soil alkali-hydrolyzable nitrogen content (AN) was determined following the alkali-hydrolyzable diffusion method. A continuous flow analyzer (AA3, Seal, Germany) was used to determine the available soil phosphorus (AP) content. The content of available potassium (AK) was determined by flame atomic absorption spectrometry. And, pH was determined using an electrode pH meter (Sartorius PB-10, Germany).

The differences in stand characteristics and productivity among different treatment plots were tested using a One-way analysis of variance (ANOVA). The assumptions of normality and homogeneous variance were examined using the Shapiro-Wilk test and Leven’s test, respectively. The means were separated by the least significant difference (LSD) test, and statistical significance was set at p<0.05. All statistical analyses were performed in R (version 3.6.2) (R Core Team, 2020), and the data were calculated using Excel 2016 (Britannica, 2019). A linear regression analysis was used to detect the relationship between the stand productivity and shoot dynamics. Lastly, Pearson correlation analysis was used to detect the relationship between stand productivity and soil nutrient content. All graphs were drawn using the ggplot2 package.

In the first year after cutting, the number of shoots and new bamboo trees in SC were significantly higher than those in RB and CK (p<0.05) ( Figure 2 ). With the natural recovery of the plots, the number of shoots and new bamboo trees gradually decreased in SC. After three years, the shoot returned rate in SC was significantly higher than that in RB and CK (p<0.05). After five years, the number of bamboo shoots and new bamboo trees in RB were significantly lower than those in SC and CK, and the shoot returned rate was significantly higher than that in SC and CK (p<0.05).

Cutting significantly reduced the average DBH, average height under branches and the average height of new bamboo trees (p<0.05). With the natural recovery of the plots, the average DBH, average height under branches, and the average height of new bamboo trees gradually increased in SC. After five years, there was no significant difference in the characteristics of new bamboo trees between SC and CK (p>0.05). The average height under branches and the average height of new bamboo trees in RB were significantly higher than those in SC and CK (p<0.05).

In the three treatment plots, the productivity of each organ of bamboo was the highest in culm, followed by branch, and then the lowest in leaf. Cutting did not reduce the productivity of Moso bamboo ( Table 2 ). Over time, the productivity of branches and culms increased gradually, while the productivity of bamboo leaves gradually decreased in SC. However, the productivity of organs in RB gradually decreased.

Further analysis showed that in the first year after cutting, the percentage of leaf productivity to the total productivity in SC was higher than that in CK and RB ( Figure 3 , p<0.05). Five years after cutting, the percentage of branch productivity to total productivity in the RB decreased significantly, while, the percentage of culm productivity to total productivity in SC increased significantly ( Figure 3 , p<0.05).

Five years after cutting, the mean height to crown base, mean height, and mean DBH of Moso bamboo in the SC were still significantly lower than those in RB and CK (p<0.05). However, the density of bamboo in the different treatment plots following RB>SC>CK. Aboveground biomass storage in SC reached the level of CK ( Table 3 ).

In the first year after cutting, the number of shoots and bamboo increased significantly. This may be due to the gibberellin content in whip buds, and soluble sugar, soluble protein, and the endogenous hormone of new bamboo increased after cutting (Wang, 2021). It was found that the increase in auxin accumulation in whip buds was the key to promoting shoot production (Wang, 2021). Soluble sugar and protein acted synergistically to promote germination and growth of shoots (Zeng et al., 2019). High gibberellin accumulation could reduce the number of bamboo shoots withdrawn (Chen et al., 2022). In addition, it was found that stronger light was beneficial to clone plants to produce more ramets (Li et al., 2018). The gap was formed in the bamboo forest after cutting, and the temperature heterogeneity of the stratum was good (Shen et al., 2020). With the recovery of the cut plots, the canopy structure returned to the control level, and the difference in temperature and humidity between the forest and the control plots decreased (Shen et al., 2020). The number of shoots and bamboo decreased significantly after the third On-year growth in RB. This was likely because the stand density was too high, and the nutrient competition among bamboo plants was fierce (Liu et al., 2016). The mean height to crown base and mean height of new bamboo gradually increased, mainly in high-density bamboo forests, by increasing the height growth to obtain nutrients. However, at the early stage of harvest recovery, the new bamboo height in SC reduction was owing to the content of non-structural carbohydrates in shoots and new bamboo was significantly lower than CK (Zeng, 2019). The carbon content in the shoot is not adequate for high growth (Song et al., 2016). In addition, trace elements in the soil could lead to the internode growth of Moso bamboo. A previous studies have found that zinc and boron deficiency can affect high growth of Moso bamboo (White, 2012).

Reducing the height of the crown base and allocating more nutrients to leaves are morphological plasticity strategies for new bamboo to adapt to strip harvesting (Zheng et al., 2021). Allocating more biomass to bamboo branches and leaves could speed up bamboo forest recovery, as higher LAI may lead to higher light capture and total biomass increase (Prado-Junior et al., 2016). In the first year after cutting, the productivity ratio of RB and SC to CK was less than 1. Cutting reduced the productivity of RB and SC ( Figure 4 ). Therefore, this verifies our hypothesis. The reduction in bamboo density in SC reduced the stand productivity (Liu, 2009). And nutrient supply from the RB to SC through physiological integration may limit the growth and development of standing bamboo in RB (Li et al., 2000). With increasing time, the productivity of SC increased, while that of RB decreased. At 5 years after cutting, the productivity of SC was higher than that of CK, while that of RB was lower than that of CK. This was owing to the high number of old bamboo (Su, 2012) and high stand density (Liu et al., 2016). Density regulation is an important measure to prevent productivity degradation of bamboo forests (Liu et al., 2016).

The physiological and metabolic levels of Moso bamboo at different ages were very different (Umemura and Takenaka, 2014). It was found that the biomass of I “du” bamboo was significantly lower than that of II “du” and III “du”, and the biomass difference between II “du” and III “du” was small. This meant that the growth rate of dry matter accumulation of Moso bamboo decreased significantly after the growth of II “du”, and the biomass accumulation of each organ of Moso bamboo gradually slowed down when the growth of III “du” grew to IV “du” (Su, 2012). We found that in CK, productivity positively correlated with the number of bamboo trees ( Figure 5 , p<0.05). In SC, shoot dynamics did not significantly relate to productivity ( Figure 5 ). The amount of standing bamboo of at II “du” and III “du” in SC was greater than that in CK, and the number of new bamboos did not decrease ( Figure 2 ). This meant that the growth of new bamboo was less than the contribution of productive, and the main contribution of productivity is the growth and development of the existing bamboo. In contrast, in RB, the number of shoots and the amount of new bamboo positively correlated with productivity. In addition, the shoot returned rate significantly negatively correlated with productivity ( Figure 5 , p<0.05). This means that the growth of new bamboo is the main factor in the change of productivity in RB.

The mineral elements needed for the growth and development of Moso bamboo include N, K, P (Embaye et al., 2005), Si, Ca (Ma and Takahashi, 2002), Zn, and B (White, 2012). In addition to N, P, and K, the contents of other elements in the soil could meet the growth of Moso bamboo (Su, 2012). Nitrogen is crucial for the yield from bamboo forests as the demand and absorption of N are highest during each growth period (Su et al., 2019). Phosphorus is an essential macronutrient for higher plants and is typically a highly mobile and frequently translocated element (White, 2012). Potassium accumulates in meristem and larval tissues and is absorbed by the roots of higher plants (White, 2012). Potassium has a high reabsorption efficiency in plants (Umemura and Takenaka, 2014).

The study found that there are obvious differences in nutrient element storage of different components (Embaye et al., 2005). As a photosynthetic organ, the contents of nitrogen, phosphorus, and potassium in bamboo leaves were significantly higher than that in other organs (Su, 2012). The storage of N and K in culm and branch is higher than that of P. Furthermore, the nitrogen content in the organs of I “du” and II “du” bamboo were slightly higher than that of III “du” bamboo, while the phosphorus content of II “du” and III “du” bamboo was higher than I “du” bamboo (Liu, 2009). According to the above analysis, the productivity in SC and CK was mainly contributed by the II “du” and III “du” Moso bamboo, and in RB was mainly contributed by the growth of new bamboo. This means that more N and P are needed for bamboo growth in SC and CK. Our correlation analysis showed that the soil contents of total N, total P, and available P positively correlated with productivity in SC and CK ( Figure 6 ). As a cofactor of various enzymes, the K content of I “du” bamboo was significantly higher than that of II “du” and III “du” bamboo (Umemura and Takenaka, 2014). In our study, the productivity of RB negatively correlated with the contents of total P, available N, and available K in soil ( Figure 6 ). This is inconsistent with the demand for bamboo management. A fertilization experiment of Moso bamboo revealed that nutrient addition could increase aboveground biomass (Ni et al., 2021). In contrast, we believe that the increase in available potassium content in the soil was due to the excessive density of standing bamboo, and rainfall brings more K⁺ to the soil through leaching (Umemura and Takenaka, 2014). Meanwhile, it is believed that the bamboo forest density restricts productivity and decouples stand productivity from soil nutrient change. Thus, it is suggested that the productivity of RB is not limited by nutrient deficiency and can be managed.

The main considerations for the feasibility of strip cutting in the bamboo forest are the nutrient supply capacity of the mother bamboo and the restoration level of strip cut plots (Zheng et al., 2022a). Strip cutting experiments on different bamboo species have been carried out in Jiangsu, Anhui, Yunnan, Sichuan, and Fujian (Zeng, 2019; Zhan, 2019; Wu, 2020; Wang, 2021), covering most of the regions of bamboo producing areas in China (Administration, 2019). Studies on strip cutting of Moso bamboo have concluded relatively consistent results regarding the setting of the cutting width and the recovery period (Zeng, 2019; Zhan, 2019; Wu, 2020). Thus, we believe that the strip cutting method is suitable for the management of moso bamboo forests nationwide. It was found that increasing the quantity and quality of bamboo in the first year after cutting is an important measure to restore productivity. It is suggested to fertilize the cutting area in advance before cutting to ensure the high-quality growth of bamboo. However, in different regions, soil nutrients, topographic environment, and climate factors are different, and the amount and types of nutrients added are different, which needs further research.