2. Materials and methods

### 2.1. Study area

also significantly contribute to food security by providing food for ruminants, which are sources of meat and milk for human consumption. China has nearly 4 million km2 of grasslands, accounting for 40% of China's total land area and 13% of the world's total grassland [4, 5]. Concurrent with population growth and socioeconomic development, however, China's grasslands have experienced rapid degradation over the last few decades due to climate change and unsound anthropogenic impacts [6, 7]. To combat the grassland degradation and restoration of the environment, the Chinese government has launched batches of national-scale conservation policies during the late 1990s and early 2000s. Two of them, the Grain for Green Program (GGP) and the Grazing Withdrawal Program (GWP) cover most of the grassland regions [8–10]. Restoring degraded grassland ecosystems is critical to the ecological and economical sustainabil-

About 90% of grassland was degraded as a consequence of overgrazing by livestock in China [11]. Overgrazing induced considerable destructive effects on plant community and soil resources [12]. Grazing exclusion has been proven to be a successful practice to restore degraded grasslands throughout the world [13, 14]. Many studies pointed out significant enhancing effects of grazing exclusion on plant coverage, density and aboveground biomass in the early stage, which were diluted or even reversed as grazing exclusion time increased [11, 15]. Meanwhile, grazing exclusion not only significantly increased storage and availability of soil water and nutrients through more litter inputs [14, 16], but also played an important role in structuring community of soil eukaryotes [17]. Contrasted with numerous researches on aboveground responses to grazing exclusion, researches about root responses are largely limited by the studying difficulties and complexity of plant roots. Current studies on fenced grassland root mainly focused on root biomass and its distribution pattern in different types of grassland [12, 18]. Root morphology and/or physiology traits and plasticity have received considerable attentions due to their capability of foraging soil nutrients [19, 20]. There is a considerable difference in root traits and plasticity among different plant species, normally with greater ones in graminaceous species [21]. The hierarchy of root trait values and plasticity among species and plant functional groups in the vegetation could drive early-stage competition for water and nutrients, which ultimately made an effect on vegetative succession [22, 23]. However, major knowledge gaps still exist, concerning responses of plant root morphological traits and root community composition to grazing exclusion in long-term

Soil respiration plays an important role in regulating soil C pools and net C balance in terrestrial ecosystems [24]. The rate of soil respiration can be influenced by climate change (global warming, precipitation regimes, etc.), as well as anthropogenic activity (land use change and management practice), with consequent impacts on terrestrial C cycling and feedbacks to climate change [25, 26]. As one of the common land use practice, clipping or mowing of hay is regarded as a critical component of global change [27]. The effect of clipping on soil respiration had been investigated widely in different ecosystems; however, the results were various and inconsistent with each other [28, 29]. One reason for the variability of previous studies in clipping effect on soil respiration is that soil respiration is composed of two different components. One of the components is root respiration, which refers to the CO2 emission from plant roots, mycorrhizal fungi and other associated microorganisms

ity of these systems.

44 Plant Ecology - Traditional Approaches to Recent Trends

restored grassland.

This research was conducted in Yunwushan National Natural Grassland Protection Zone in Ningxia Hui Autonomous Region, China (36�10<sup>0</sup> -36�17<sup>0</sup> N, 106�21<sup>0</sup> -106�27<sup>0</sup> E, 1800–2100 m a.s.l.). Since 1982, the grassland has had been protected as a long-term monitoring sites for restoration of degraded grassland. The site is located at an elevation from 1800 to 2100 m and has a total area of 6660 ha. Mean annual temperature during 1982–2011 was 7�C with mean monthly temperature extremes of �22�C in January and 25�C in July. Annual precipitation averaged 425 mm. Annual evaporation is 1017–1739 mm, and the frost-free season averages 137 days. Soil type in the study area is montane gray-cinnamon soil. The vegetation community consists of 297 plant species and is dominated by Stipa plants (Stipa bungeana, Spectrunculus grandis, Salvia przewalskii), and main forbs include Artemisia sacrorum and Thymus mongolicus.

### 2.2. Experimental design and sampling

### 2.2.1. Grazing exclusion

Five experimental sites along a chrono-sequence of grassland restoration were selected in August 2012, when peak aboveground biomass occurred, with grazing exclusion for 30 years (GE30), 22 years (GE22), 9 years (GE09), 5 years (GE05) and continuous grazing at a medium density during the whole year (four sheep/ha) (GG), respectively. A transect of 300 � 100 m with representative vegetation was selected as the study area within each site, in which three pseudo-replicated plots (30 � 30 m) were established, and three subplots (2 � 2 m) were set up with a minimum interval of 15 m in each plot for field sampling.

### 2.2.2. Soil sampling

With aboveground plant parts being attached, a soil block of 50 cm long � 50 cm wide � 30 cm deep was excavated in each subplot and then was gently loosen by hand to get the intact rootsoil mixtures with minimal breakage. Plant root-soil mixtures were soaked in water for twenty minutes and were gently shaken for several times to remove bulk soil.

### 2.2.3. Plant root sampling

Plant roots were carefully washed under flowing water to remove tightly attached organic matter and mineral soils and carefully identified roots in plant functional group level according to plant aboveground parts, root color, diameter, branches and texture. Five functional groups (PFGs) were categorized as perennial rhizome grass (PR), perennial bunchgrass (PB), PF perennial forbs (PF), shrubs and semishrubs (SS) and annuals and biennials (AB) [26, 41]. Functional group richness and species richness were the number of functional groups and plant root species appearing in one subplot, respectively.

After cutting down plant aboveground parts, roots in the same plant functional groups were spread on a transparent, plastic tray and scanned at a resolution of 300 dpi (Epson Scanner (10000XLPro, Canada). Root images were analyzed with WinRhizoPro software (V2012b, Regent Instruments, Canada) to measure root length (m), root surface area (cm2 ). Thereafter, roots were oven-dried at 65�C for 48 h and then weighed to gain root mass. Root biomass, root length density, specific root length and specific root area are calculated in equations [42] as follows:

$$\text{Root biomass } (\text{RB}, \text{ g } \text{m}^{-2}) = \text{Root mass} / \text{Sampling area} \tag{1}$$

$$\text{Root length density } (\text{RLD}, \text{ mm}^{-3}) = \text{Root length/Sampling volume} \tag{2}$$

$$\text{Specific root length (SRL, }\text{mg}^{-1}) = \text{Root length/Root mass} \tag{3}$$

$$\text{Specific root area } (\text{SRA, } \text{cm}^2 \text{ g}^{-1}) = \text{Root surface area/Root mass} \tag{4}$$

#### 2.2.4. Clipping management

temperature extremes of �22�C in January and 25�C in July. Annual precipitation averaged 425 mm. Annual evaporation is 1017–1739 mm, and the frost-free season averages 137 days. Soil type in the study area is montane gray-cinnamon soil. The vegetation community consists of 297 plant species and is dominated by Stipa plants (Stipa bungeana, Spectrunculus grandis, Salvia

Five experimental sites along a chrono-sequence of grassland restoration were selected in August 2012, when peak aboveground biomass occurred, with grazing exclusion for 30 years (GE30), 22 years (GE22), 9 years (GE09), 5 years (GE05) and continuous grazing at a medium density during the whole year (four sheep/ha) (GG), respectively. A transect of 300 � 100 m with representative vegetation was selected as the study area within each site, in which three pseudo-replicated plots (30 � 30 m) were established, and three subplots (2 � 2 m) were set up

With aboveground plant parts being attached, a soil block of 50 cm long � 50 cm wide � 30 cm deep was excavated in each subplot and then was gently loosen by hand to get the intact rootsoil mixtures with minimal breakage. Plant root-soil mixtures were soaked in water for twenty

Plant roots were carefully washed under flowing water to remove tightly attached organic matter and mineral soils and carefully identified roots in plant functional group level according to plant aboveground parts, root color, diameter, branches and texture. Five functional groups (PFGs) were categorized as perennial rhizome grass (PR), perennial bunchgrass (PB), PF perennial forbs (PF), shrubs and semishrubs (SS) and annuals and biennials (AB) [26, 41]. Functional group richness and species richness were the number of functional groups

After cutting down plant aboveground parts, roots in the same plant functional groups were spread on a transparent, plastic tray and scanned at a resolution of 300 dpi (Epson Scanner (10000XLPro, Canada). Root images were analyzed with WinRhizoPro software (V2012b, Regent

oven-dried at 65�C for 48 h and then weighed to gain root mass. Root biomass, root length density, specific root length and specific root area are calculated in equations [42] as follows:

). Thereafter, roots were

Þ ¼ Root mass=Sampling area ð1Þ

Þ ¼ Root length=Sampling volume ð2Þ

przewalskii), and main forbs include Artemisia sacrorum and Thymus mongolicus.

with a minimum interval of 15 m in each plot for field sampling.

minutes and were gently shaken for several times to remove bulk soil.

and plant root species appearing in one subplot, respectively.

Instruments, Canada) to measure root length (m), root surface area (cm2

Root biomass <sup>ð</sup>RB, g m�<sup>2</sup>

Root length density <sup>ð</sup>RLD, mm�<sup>3</sup>

2.2. Experimental design and sampling

46 Plant Ecology - Traditional Approaches to Recent Trends

2.2.1. Grazing exclusion

2.2.2. Soil sampling

2.2.3. Plant root sampling

The experiment was designed as a randomized block with five replicate blocks. Clipping was done once a year in the spring (June 20, 2014, and June 16, 2015). The trenching method was used in this study to separate soil respiration into root and microbial respiration [43]. In each plot, one root-free small plot (0.3 � 0.3 m) lined with nylon mesh (0.038 mm mesh size) in 0.5 m deep was randomly assigned. Soil respiration and its components were measured using an LI-6400 portable photosynthesis system attached to a soil CO2 flux chamber (800 cm<sup>3</sup> in total volume; LI-COR 6400-09 TC, LI-COR Inc., Lincoln, NE, USA). The CO2 efflux measured in the root-free plots reflects only microbial respiration, while CO2 efflux measured in the whole-soil plots (roots are not removed) resulted from both microbial and root respiration. The difference between the CO2 efflux values for root-free plots and whole-soil plots was used to indicate root respiration. However, we observed that the soil temperature and moisture in root-free plots were significantly higher than those in whole-soil plots. The actual root respiration would be underestimated if it is directly calculated from the difference of measured CO2 flux between the whole-soil plot and root-free plot. To eliminate this error, we corrected the measured microbial respiration by using the linear Eq. (5), simulating the relationship between microbial respiration, soil temperature and soil moisture in root-free plots:

$$MR\_{\text{measured}} = a \times T + b \times W + c \tag{5}$$

where MRmeasured, Tand Ware the microbial respiration (μmol CO2 m�<sup>2</sup> s �1 ), soil temperature (�C) and volumetric soil water content (%) measured in the root-free plot, respectively. a, b and c are coefficients relevant to soil temperature and moisture.

Then, we determined the corrected microbial respiration (MRcorrected) using the soil temperature and moisture in the whole-soil plot. Root respiration (RR) calculated by the difference between the SR and the MRcorrected is as follows:

$$RR = SR - MR\_{corrected} \tag{6}$$

Soil temperature at the depth of 5 cm was determined using a thermocouple probe connected to the LI-6400 adjacent to each PVC collar, and volumetric soil content in the 0–10 cm soil layers was measured using a TRIME TDR probe (IMKO, Ettlingen, Germany) adjacent to the same sites after soil temperature measurements. The root length production was measured using the minirhizotrons technique [44]. Peak aboveground biomass (AGB) was estimated by harvesting plant tissues above the soil surface from one 0.5 � 0.5 m quadrats at each plot in late September of both years. After aboveground plant residues cleaned, soil samples to depths of 10 cm were collected. Roots were collected from soil samples to determine belowground biomass (BGB). WSOC was measured using an automated total organic C analyzer (TOC-Vcph, Shimadzu, Japan) [45]. SMBC was determined using the chloroform fumigation extraction method [46].

### 2.2.5. Biomass data collection

Field harvest was conducted in mid or late August each year from 1982 to 2011, when the standing biomass reached its maximum. For each harvest in each year, 15 quadrats (1 1 m) were selected along a transect (300 100 m). Aboveground biomass was clipped and dried at 65C to constant weight. Between 1982 and 1992, the degraded grassland recovered rapidly and biomass production increased almost linearly. It was mainly caused by the exclusion of human disturbance, particularly overgrazing. After 1992, grasslands assumed a relatively balanced state with lower variation in productivity and diversity. Further variation in productivity was likely caused primarily by climatic variation. We therefore used the peak aboveground biomass during 1992–2011 to evaluate the impacts of climate variability on grassland productivity. Mean daily temperature and precipitation during 1992–2011 were obtained from a weather station established in 1982, located only 0.9 km from the surveyed transect.

### 2.3. Data analyses

### 2.3.1. One-way analysis of variance

A one-way analysis of variance (ANOVA) followed by Tukey's HSD test was conducted to determine the effect of grazing exclusion time on grassland root traits (RB, RLD, RSA, plant functional group richness, plant species richness), the differences of root traits (SRL, SRS) and proportion in root community between plant functional groups, and the effects of clipping over time on soil respiration, microbial respiration, root respiration, soil temperature and soil moisture. Differences were considered significant for all statistical tests at P < 0.05. All the statistical analyses were conducted using IBM SPSS 18.0 (IBM, USA). Graphs were created with Sigma plot 12.5 (Systat Software, USA).

### 2.3.2. Partial least squares

Partial least squares (PLS) regression was used to analyze the responses of grassland productivity to variation in daily temperature and precipitation during all 365 days of the year based on data for 1992–2011. The two major outputs of PLS analysis are the variable importance in the projection (VIP) and standardized model coefficients. The VIP values reflect the importance of all independent variables for explaining variation in dependent variables. The VIP threshold for considering variables as important is often set to 0.8. The standardized model coefficients indicate the strength and direction of the impacts of each variable in the PLS model. The root-mean-square errors (RMSEs) of the regression analyses were calculated to determine the accuracy of the PLS model. In the PLS analyses, periods with VIP greater than 0.8 and high absolute values of model coefficients represent the relevant phases influencing grassland productivity. Positive model coefficients indicate that increasing temperature or precipitation during the respective period should increase ANPP, while negative model coefficients imply negative impacts on productivity.
