Wetness modulates the effects of grazing on net ecosystem productivity in global grasslands

Yueqiang Wu; Le Qi; Hao Li; Jiguang Feng; Peng Zhou; Hangyu Li; Xiaoyang Gao; Zhijie Wang; Shilin Cui; Ping Yin; Wenhong Ma; Cunzhu Liang; Zhiyong Li; Biao Zhu

doi:10.7554/eLife.110725.2

eLife Assessment

This important study combines a five-year field experiment with a meta-analysis to quantify effects of grazing on ecosystem CO₂ fluxes as modified by wetness. The evidence supporting the conclusions is convincing, but several aspects of the methods and discussion would benefit from clarification to improve reproducibility and interpretability.

https://doi.org/10.7554/eLife.110725.2.sa2

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Abstract

Overgrazing has induced widespread grassland degradation globally. However, how grazing intensity affects ecosystem carbon dioxide (CO₂) fluxes with wetness fluctuations in grasslands at a global scale remains poorly understood. Here we measured ecosystem CO₂ fluxes covering the 7th to 11th years of a continuous grazing experiment in a typical steppe, and conducted a meta-analysis of 585 observations by collecting relevant data in global grasslands. Our experimental results showed that mean gross primary productivity (GPP) and ecosystem respiration (ER) were decreased under heavy grazing (HG) compared with control without grazing (CK), whereas net ecosystem productivity (NEP) was not different between HG and CK in the typical steppe. The NEP was higher under light grazing in the wetter years, but it was not different between LG and CK under drier years in the typical steppe. The structural equation model showed that grazing altered NEP by increasing soil temperature and relative growth rate, while decreasing AGB. In contrast, grazing decreased GPP, ER and NEP, respectively, in global grasslands. Both our field experiment and the meta-analysis revealed that the response of NEP to light grazing, rather than heavy grazing, was lineally correlated with the wetness index. Higher wetness and aboveground biomass (AGB) increased the response of NEP to grazing in global grasslands. However, heavy grazing reduced NEP and AGB even under higher wetness indices in global grasslands, resulting from a loss of their resilience in long-term heavy grazing. These findings indicate that light grazing appears to be a promising management to promote plant relative growth rate and carbon sequestration. Overall, the meta-analysis and field experiment jointly provide global perspectives on the response of ecosystem CO₂ fluxes to grazing intensity and improve our understanding of the factors influencing the response of ecosystem CO₂ fluxes to grazing intensity.

Introduction

Globally, grassland ecosystems cover approximately one fifth of the world land surface and contain about 20-30% of the global pool of soil organic carbon (SOC) (Du et al., 2022). Grasslands also play a vital role in livestock production, promoting carbon (C) sequestration and regulating climate change (Heimann & Reichstein, 2008; Zhou et al., 2017). Grazing is a widespread land-use activity that alters terrestrial C dynamics (He et al., 2020; Zhou et al., 2025). However, grassland ecosystems have seriously deteriorated globally (Zhang et al., 2025). Grassland degradation is influenced by a multitude of driving factors, such as overgrazing and precipitation limitation (Sloat et al., 2018). Furthermore, the C balance of grasslands is also increasingly influenced by changes in grazing management practices and water availability (Hilker et al., 2014; Lemoine et al., 2016; Ren et al., 2025). Overgrazing and drier conditions could potentially drive grasslands from acting as C sinks to C sources (Hao et al., 2018; Morgan et al., 2016; Shao et al., 2013).

Gross primary productivity (GPP) and ecosystem respiration (ER) are critical processes governing C sequestration and emission in terrestrial ecosystems (Chen et al., 2023; Knauer et al., 2023). Net ecosystem productivity (NEP) is the balance between ecosystem C uptake and release, and serves as a key indicator of the net C exchange (Niu et al., 2010; Zhang et al., 2024). Grazing influences NEP by altering GPP and ER (Fig. 1). Grazing directly removes aboveground plant tissues through herbivory, altering vegetation cover, soil temperature, and aboveground biomass (AGB) and thereby influencing plant photosynthesis and GPP (Owensby et al., 2006; Zhou et al., 2007). Plants with high relative growth capacity may rapidly restore leaf area and photosynthesis tissues after grazing, potentially maintaining or even enhancing GPP (Oesterheld & McNaughton, 1991; Penner & Frank, 2021). In contrast, heavy grazing or water limitation may constrain plant regrowth, limit soil moisture, leaf area recovery, and reduce GPP. Grazing may also affect ER by reducing litter and root carbon inputs, altering belowground carbon allocation, and modifying soil bulk density, porosity, water infiltration, and aeration through livestock tamping, with consequences for root respiration and microbial decomposition (Bardgett & Wardle, 2003; Wu et al., 2023; Xun et al., 2018). Variation in NEP responses to grazing may depend on whether post-grazing recovery of GPP can compensate for carbon losses through ER. Thus, plant relative growth rate and compensatory growth capacity may represent important mechanistic traits linking grazing disturbance, photosynthesis recovery, GPP dynamics, and NEP response.

Conceptual illustration of how grazing with wetness fluctuation would affect net ecosystem productivity (A), and geographic (B) and climatic distributions of data (C) included in this meta-analysis.

Assessing ecosystem carbon dioxide (CO₂) fluxes (GPP, ER and NEP) in grasslands is essential to the terrestrial C cycle at local, regional and global scales. However, current evidence on the effect of grazing on ecosystem CO₂ fluxes in grasslands remains inconsistent, with studies reporting both positive (Chang et al., 2020; Du et al., 2022) and negative (Liu et al., 2020; Wang et al., 2023; Zhu et al., 2015) effects. For instance, some studies demonstrated that grazing decreased GPP and ER (Nakano et al., 2020; Qi et al., 2024; Wang et al., 2025), while others reported an increase or no difference in GPP or ER (Chang et al., 2020; Luo et al., 2015; Shi et al., 2022). Similarly, some studies investigated that grazing significantly reduced NEP (Wang et al., 2023; Zhu et al., 2015), while others reported an increase or no difference in NEP under grazing (Du et al., 2022; Gomez-Casanovas et al., 2018; Yu et al., 2025). The variations in the responses of ecosystem CO₂ fluxes are likely driven by differences in grazing intensities, climatic variations, livestock and grassland types (Hou et al., 2016; Wang et al., 2023; Xiao et al., 2011; Zhu et al., 2015).

The effects of grazing intensities on ecosystem CO₂ fluxes in global grasslands were also varied in different studies (Jiang et al., 2020; Rong et al., 2017; Zhang et al., 2022). Specifically, heavy grazing usually reduced the GPP, ER and NEP (Ju et al., 2024; Rong et al., 2017; Zhang et al., 2022), but a meta-analysis also reported the response of NEP was increased in Chinese grasslands (Jiang et al., 2020). Generally, light grazing can enhance NEP by promoting plant regeneration and improving photosynthetic efficiency, whereas moderate to heavy grazing tends to reduce NEP, largely as a result of decreased vegetation coverage and plant biomass (Yu et al., 2025). Conversely, a meta-analysis showed that the response of NEP was decreased under light and moderate grazing (Zhang et al., 2022). However, it was reported that moderate grazing led to the highest NEP (Zhu et al., 2015). These conflicting results underscore the complex responses of ecosystem CO₂ fluxes in grasslands to grazing and highlight the necessity to identify key factors controlling ecosystem CO₂ flux dynamics under grazing management. This understanding is essential for accurately modelling and predicting grassland C dynamics under global change.

Many studies have demonstrated that interannual variation in grassland NEP can be substantial, primarily driven by fluctuating climatic conditions, as they can directly regulate vegetation growth via altering soil moisture and temperature (Polley et al., 2008; Rong et al., 2017; Shen et al., 2016). Annual precipitation is one of the climatic parameters, while the wetness index (WI) serves as a more integrative climatic indicator that incorporates both precipitation and temperature, thereby reflecting the overall water surplus or deficit (Song et al., 2019). A higher wetness index (WI > 30) indicates sufficient water availability for plant growth, whereas a lower wetness index (WI ≤ 30) suggests the water availability may be limited (De Martonne, 1926). Considerable uncertainty remains regarding how NEP responds to grazing under different water conditions. Although evidence suggested that, under wet year, grazing significantly increased NEP; under dry year, grazing suppressed NEP and can even turn NEP negative (Shao et al., 2013). In general, grazing tends to have a less suppressive effect on NEP under wet than dry conditions (Okach et al., 2019; Zhou et al., 2019). For example, heavy grazing consistently reduced NEP, with a more pronounced decline occurring during dry years in a desert steppe (Jin et al., 2023). In addition, overgrazing led to decoupling of precipitation patterns and ecosystem carbon exchange in the desert steppe, primarily by altering plant community composition (Wang et al., 2023). However, it remains unclear how NEP responds to grazing intensities under water availability fluctuations across global grasslands.

Previous studies on the effects of grazing intensities on ecosystem CO₂ fluxes have been constrained by limited spatial and temporal scales, which has led to an incomplete understanding of how different grazing intensities influence ecosystem CO₂ fluxes. Furthermore, does the wetness modulate the effect of grazing on ecosystem CO₂ fluxes in the typical steppe? Are these relationships globally generalizable? In this study, we investigated the effects of grazing intensities on ecosystem CO₂ fluxes by combining a long-term (7-11 years) field experiment conducted in a typical steppe and a meta-analysis of global grasslands. The objectives of this study were to: (i) investigate the effects of grazing intensity with annual wetness fluctuations on ecosystem CO₂ fluxes (GPP, ER and NEP) covering the 7th to 11th years of a continuous grazing experiment in a typical steppe as well as the meta-analysis in global grasslands; (ii) explore how environmental factors (particularly wetness index, soil moisture and temperature, and grazing intensity) regulate the effects of grazing on ecosystem CO₂ fluxes.

Materials and methods

2.1. Experimental site

The grazing experiment was conducted at Xilinhot National Climate Observatory (44°12′ N, 116°19′ E, 1129 m above sea level), in the middle part of Inner Mongolian typical steppe in China. The average annual precipitation was 291 mm from 2019 to 2023, with average precipitation in the growing season (May to September) varying from 168 to 325 mm (Fig. S1). The average mean air temperature was 3.9 (from 2019 to 2023). The soil is classified as a Haplic Calcisol based on the FAO soil classification system or a Calcic-Orthic Aridisol based on the USDA soil classification system (Liang et al., 2021). The vegetation community is dominated by Stipa grandis P. Smirn (perennial bunchgrass) and Leymus chinensis Trin. Tzvel (perennial rhizomatous grass) (Wu et al., 2024).

2.2. Experimental design

The grazing experiment was initiated in 2013 and was based on a randomized design (Fig. S2). There were four treatments with three replicates: control without grazing (CK), light grazing (LG, 0.64 sheep ha^-1 year^-1), moderate grazing (MG, 1.28 sheep ha^-1 year^-1) and heavy grazing (HG, 2.56 sheep ha^-1 year^-1) conducted in plots with 1.44 ha (120 × 120 m²) established in the research station (Fig. S2). Two-year-old sheep similar in size were selected to use for grazing from May to September in each year. Sheep were grazing from 7:00 AM to 6:00 PM each day, and then gathered into an enclosure at night (Liang et al., 2021).

2.3. Measurement of ecosystem CO₂ fluxes

Ecosystem CO₂ fluxes were measured using a transparent chamber (0.5 × 0.5 × 0.8 m³) connected to an infrared gas analyzer (LI-8100, Lincoln, NE, USA). To ensure an airtight seal during measurements, the junction between the chamber bottom and the aluminum base was secured with tapes. After positioning the chamber, the enclosed air was continuously mixed by two fans in the chamber. The CO₂ concentrations were recorded 12 times at 10-second intervals over a 2-minute period. The NEP was then determined from the rate of CO₂ concentration change, combined with chamber volume and headspace temperature data. A positive NEP value indicated net ecosystem CO₂ uptake, whereas a negative value denoted net ecosystem CO₂ release. Following each NEP measurement, the chamber was removed to vent the headspace and then covered with a dark cloth to inhibit photosynthesis before being repositioned on the same base for ER measurement. Both the NEP and ER were measured throughout the growing seasons from 2019 to 2023, corresponding to the 7th to 11th years of grazing. The GPP was thus calculated as the sum of NEP and ER using data collected over this five-year period. The gas exchange measurements were conducted on sunny and calm days between 9:00 and 11:00, a time when the ecosystem CO₂ fluxes represented the daily average (Niu et al., 2008; Rong et al., 2017; Yu et al., 2025), with a frequency of three times per month. In each plot, the chambers of two replicates were measured. Thus, six replicates per treatment were set up in the field experiment.

2.4. Climate conditions and soil microclimate

The annual precipitation and mean annual air temperature (MAT) for the field study area from 2019 to 2023 were obtained from the China Meteorological Data Service Center (http://data.cma.cn/). The precipitation and air temperature were collected from the Xilinhot Meteorological Observation Station, which was near the experimental site in this field experiment. The De Martonne wetness index (WI) was calculated as follows (De Martonne, 1926; Song et al., 2019):

Soil temperature and moisture at a depth of 0-10 cm were measured simultaneously with ecosystem CO₂ flux measurements, using the probes of the LI-8100 system. Soil moisture was the soil volumetric water content. Measurement sites for soil temperature and moisture were selected near the collars randomly placed in the plots. They were investigated synchronized with the measurements of ecosystem CO₂ fluxes.

2.5. Plant sampling and analyses

Four grazing rotations were conducted each year from 2019 to 2023 in the field study. Before each grazing rotation, two cages (1.2 m × 1.2 m × 1.2 m) were installed in each plot to ensure that the vegetation inside would not be foraged by sheep. Aboveground plant biomass was collected after the end of each grazing rotation. Specifically, in each plot, five 1 m × 1 m quadrats were placed. All AGB within these quadrats was clipped at ground level and then oven-dried at 65 °C for 48 h to determine the community-level dry biomass. The plant species were classified into C₃ and C₄ groups. Belowground biomass (BGB) was quantified by collecting root biomass using soil core with diameter of 7 cm each September from 2019 to 2023 (Fig. S2). Specially, two soil cores were collected from each 1 m × 1 m quadrat corresponding to BGB measurements at depths of 0-30 cm. The belowground parts were then extracted from the soil by washing with water and oven-dried at 65 °C for 48 h to obtain dry weight.

The relative growth rate (RGR) was defined as the ratio of aboveground biomass regeneration to the number of days between two consecutive grazing events and was calculated by Eq. (2) (Zong & Shi, 2019):

where AGBE_i+₁: the AGB inside the cages after the (i+1)-th grazing round, AGB_i: the remaining AGB after the i-th grazing, △t: the number of days between the i-th and (i+1)-th grazing rounds.

Aboveground net primary productivity (ANPP) was estimated as the sum of the total herbage consumed by livestock throughout the growing season and the AGB measured at the end of the final grazing rotation of the season.

2.6. Data Analysis

All the analyses were conducted using the R (version 4.4.3) (Team, 2023). Annual mean values were calculated for GPP, ER, NEP, AGB, BGB, root/ shoot ratio, soil temperature, and soil moisture from 2019 to 2023. Differences across years and among treatments within each year were examined using the Least Significant Difference (LSD) test. Response ratios of GPP, ER, NEP, AGB, BGB, root/ shoot ratio, soil temperature, and soil moisture were calculated based on Eq. (3). Additionally, linear regression analysis was conducted using the “lm” function to evaluate the relationships of GPP, NEP, AGB, BGB, and RGR with WI.

Where X_T and X_C are the mean values of the abovementioned indicators for the grazing (T) and control (C) treatments, respectively.

The repeated measures ANOVA (the ezANOVA function of the “ez” R package) was used to examine the effects of grazing treatment and sampling years on ecosystem CO₂ fluxes (GPP, ER, and NEP), microclimatic variables (soil temperature and moisture), and plant biomass parameters (AGB, BGB, and root/ shoot ratio) across the five-year study period from 2019 to 2023 (Chen et al., 2024).

We also conducted a random forest model to identify the significant environmental predictors of GPP, ER, NEP, AGB and BGB. The environmental predictors considered in the field study included grazing intensity, the response ratio of soil temperature and soil moisture, wetness index, and grazing duration. The random forest model was implemented in R (version 4.4.3) using the “randomForest” and “rfPermute” packages. In this model, the importance of each predictor was evaluated based on the percentage increase in the mean squared error (MSE) when that predictor was permuted; a higher MSE value indicated greater importance of the factor. The MSE values for each decision tree, based on out-of-bag estimates from the random forest model, were generated using the “rfPermute” package to assess the relative importance of the predictor variables (Liao et al., 2024). To evaluate the direct and indirect effects of grazing intensity, wetness index, soil temperature, soil moisture, RGR, AGB, and C₃and C₄ plant biomass and richness on NEP, we conducted a structural equation modeling (SEM) analysis using the ‘piecewiseSEM’ packages. A good fit of the SEM was evaluated using Fisher’s C value, which was non-significant (P > 0.05) (Yang et al., 2025).

2.7. Meta-analysis of grazing experiments in global grasslands

To assess the effects of grazing on GPP, ER, and NEP in global grasslands, we conducted a systematic literature search for relevant papers published up to June 8, 2025, using the Web of Science (http://apps.webofknowledge.com/) and China National Knowledge Infrastructure (CNKI, https://www.cnki.net). The keywords and terms used were (“grazing” OR “stocking” OR “livestock” OR “grazing intensity”) AND (“net ecosystem productivity” OR “net ecosystem exchange” OR “gross ecosystem productivity” OR “ecosystem respiration” OR “soil carbon flux” OR “soil CO₂” OR “soil carbon flux” or “soil carbon emission”). Relevant studies were selected based on the following criteria (Fig. S9): (a) Studies were conducted using simulating grazing experiments, and studies with trampling and mowing were excluded. (b) The control and grazing plots were under similar conditions, such as dominant species, community composition, soil type and climate. (c) Data from non-grazed treatments were excluded when additional treatments (e.g., fertilization, experimental warming, or precipitation manipulation) were present. (d) The mean and sample size for any measurements of GPP, ER, and NEP under both grazing and control conditions must be reported. (e) The classifications of grazing intensity (light, moderate, and heavy grazing) were primarily based on the definitions provided in the original studies. For studies in which grazing intensity was not explicitly reported, we classified grazing intensity according to the modified Grazing proposed by the USDA (https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5109714.pdf) (Yin et al., 2023). Finally, 62 published papers with 585 observations of GPP, ER, and NEP responses (n = 161, 249, and 175, respectively) were selected (Fig. 4), and the geographical location of these observations were presented in Fig. 1. Meanwhile, the study site, elevation, MAP, MAT, and grazing intensity from the selected papers were recorded. The wetness indices based on MAP and MAT of each site were calculated according to Eq. (1). Where available, data for soil temperature, soil moisture, AGB, BGB, and root/ shoot ratio were also recorded.

In addition, to better explore the effects of grazing intensity and environmental factors on ecosystem CO₂ fluxes, the data was classified into different subgroups by grazing intensity (LG, MG, and HG), mean annual temperature (≤5 and >5), wetness index (≤ 30 and > 30), and precipitation (≤ 400 mm and > 400 mm), grassland type (desert grassland, temperate grassland, alpine grassland, savanna, and others), and livestock type (cattle, sheep, and mix).

The effect size of grazing on each variable was quantified using the natural logarithm of response ratios (RR) based on Eq. (3) (Feng et al., 2023).

The weighing of the ith study (W_RR) was calculated as (Pittelkow et al., 2015):

where N_T and N_C are the replicate numbers of treatment and control, respectively.

A linear mixed-effects model, with ‘study’ included as a random factor, was employed to estimate the weighted response ratio (RR₊₊) across studies or within a specific group, fitting with restricted maximum likelihood using the ‘lmer’ function in the ‘lme4’ package (Feng et al., 2023).

where β₀ is the coefficient, π_Study denotes the random effect associated with ‘study’ (accounting for autocorrelation among observations from the same study), and corresponds to the residual sampling error. We checked the normality of the model residuals using the ‘check_normality’ function in the ‘performance’ package. When the assumption of normality was violated, bootstrapping with 999 iterations was performed using the ‘boot’ package to derive the 95% confidence interval (CI) for each RR₊₊ (Chen et al., 2021). The percentage change (%) of the response ratio was calculated as follows:

The lnRR₊₊ was considered statistically significant if its 95% confidence interval (CI) did not overlap zero (Chen et al., 2021).

The random forest model was conducted using “randomForest” and “rfPermute” packages to identify the relative importance of the predictor variables (wetness index, grazing duration and intensity, the responses of soil moisture and temperature and livestock types) of GPP, ER, NEP, AGB and BGB in the meta-analysis (Liao et al., 2024).

Results

3.1 Grazing intensity effects on ecosystem CO₂ fluxes and plant biomass in the field experiment

Continuous heavy grazing reduced the mean GPP by 29.87% and the mean ER by 30.94% in the grazing experiment; however, NEP did not differ between HG and CK (Fig. 2). The mean GPP and ER were decreased by 17.46% and 22.72%, respectively, in MG than in CK, but NEP was not different between MG and CK (Table S2). The mean ER was decreased by 21.49% and 29.84%, respectively, in MG and HG than in LG. The mean NEP was decreased by 37.41% in HG than in LG, but was not different among LG, MG and CK. The GPP and NEP were 29.27% and 85.83% higher, respectively, in LG than in CK, but the ER was not different in 2020, the year with the highest precipitation and wetness index among the five years (Fig. S1 and 2). The mean aboveground and belowground biomass was decreased by 39.27% and 19.44%, respectively, in HG than in CK, but the mean root shoot ratio was increased by 30.59% in HG than in CK (Fig. 2; P < 0.05).

Effects of different grazing intensities on ecosystem carbon dioxide (CO₂) fluxes and biomass in our field experiment.

CK, enclosure; LG, light grazing; MG, moderate grazing; HG, heavy grazing. Data (means ± SE, n = 3) followed by different lowercase letters indicate differences at P < 0.05 between treatments.

Wetness was the most important predictor of NEP, while grazing intensity was the most important predictor of GPP and ER, revealing a strong coupling between CO₂-driven changes in wetness index and grazing intensity (Fig. S3). Grazing intensity and duration were also the important predictors influencing AGB and BGB. The linear regression analysis showed that overall grazing as well as light grazing stimulated the responses of GPP and NEP with increasing wetness index (Fig. 3 and Table S3; P < 0.05). This relationship could be explained by the significant positive correlation between NEP and both ANPP and RGR (Fig. S4). In the wettest year of 2020, the relative growth rate peaked relative to other years, reaching levels 3.16, 2.76 and 1.96 times higher under LG, MG, and HG respectively, compared to CK (Fig. S5). The NEP increased with WI under HG (Fig. S6; P < 0.01), whereas the response ratio of NEP to heavy grazing was not significantly correlated with WI (Fig. 3B). Grazing reduced the response of GPP, ER, AGB, BGB and soil moisture in the field experiment (Fig. 4A). Heavy grazing decreased C₃ plant biomass but increased C₄ plant richness compared with other treatments (Fig. S7). In addition, the structural equation model showed that grazing altered net ecosystem productivity by increasing soil temperature and relative growth rate, while decreasing aboveground biomass of plants (Fig. S8).

Relationships of the responses of gross primary productivity (GPP) and net ecosystem productivity (NEP) with potential regulating factors from 2019 to 2023 under different grazing intensity.

Overall, overall grazing included light, moderate and heavy grazing; LG, light grazing; MG, moderate grazing; HG, heavy grazing. The significant regression lines and 95% confidence intervals are shown with lines (solid for significant) and shaded areas, respectively.

3.2 Synthesis of grazing on ecosystem CO₂ fluxes and plant biomass across global grasslands

In the meta-analysis across global grasslands, grazing significantly decreased ecosystem CO₂ fluxes by 13.56% to 30.46% (Fig. 4B). Heavy grazing had a more pronounced negative effect on ecosystem CO₂ fluxes compared to light and moderate grazing (Fig. 5A). The response of GPP was decreased by 15.89%, 11.88% and 26.34%, respectively, under LG, MG and HG. The response of ER was decreased by 7.42%, 14.91% and 21.82%, respectively, under LG, MG and HG. The response of NEP was decreased by 18.86% and 34.89%, respectively, in LG and HG, but was not significant in MG. Light, moderate and heavy grazing reduced AGB by 27.89%, 34.57% and 66.17% (Fig. 5B). Moderate and heavy grazing decreased the BGB by 14.85% and 26.13%, respectively, but the response of BGB was not significant under light grazing. Grazing increased the root shoot ratio by 31.78% to 111.11%. Heavy grazing had a more pronounced positive effect on root/ shoot ratio compared to light and moderate grazing.

Effects of grazing on gross primary productivity, ecosystem respiration, net ecosystem productivity, aboveground biomass, belowground biomass and root shoot ratio, soil moisture and soil temperature, in global grasslands in the field experiment in this study (A) and from the meta-analysis (B).

Circles and error bars represent average parameter estimates and 95% confidence interval (CI). The star (*) indicates significance when the CI did not overlap zero. Sample sizes of observations for each variable are displayed on the left.

Effects of different grazing intensities on ecosystem CO₂ fluxes, biomass and soil temperature and moisture in global grasslands from the meta-analysis.

Circles and error bars represent average effects and their 95% confidence interval (CI). The star (*) indicates significant effects, i.e., the 95% CI did not overlap zero. The sample size for each variable is shown on the left. LG, light grazing; MG, moderate grazing; HG, heavy grazing.

Globally, grazing decreased GPP, ER and NEP under lower precipitation conditions (≤ 400 mm), while the reductions were not significant under higher precipitation conditions (> 400 mm) (Fig. S10A). Grazing significantly reduced GPP, ER, and NEP at mean annual temperatures ≤ 5, but these effects were not significant at temperatures > 5 (Fig. S10B). Similarly, under lower wetness conditions (WI ≤ 30), grazing decreased GPP, ER, and NEP more than under higher wetness conditions (WI > 30) (Fig. 6). The response of NEP was decreased under lower WI (WI ≤ 30) in MG and HG, but it was not different under higher WI (WI > 30) in MG. The response of NEP was decreased under higher WI (WI > 30) in HG. Higher wetness index mitigated the response of NEP to grazing. The duration of grazing also influenced its effect: GPP and ER declined more substantially under longer grazing durations (Fig. S10C). The most pronounced decline in NEP occurred under grazing durations of 5 to 10 years (P < 0.001). Grazing decreased GPP, ER and NEP in desert and temperate grassland, but had no significant effect on GPP, ER and NEP in alpine grassland and savanna (Fig. S11). In addition, cattle and sheep grazing decreased GPP and NEP, but livestock mixed grazing did not show significant effect on GPP, ER and NEP (Fig. S12).

Responses of ecosystem CO₂ fluxes and biomass to grazing (A) and different grazing intensities (B-C), at wetness index ≤ 30 and wetness index > 30 in global grasslands from the meta-analysis.

Circles and error bars represent average parameter estimates and 95% confidence interval (CI). The sample size for each variable is shown on the left. LG, light grazing; MG, moderate grazing; HG, heavy grazing.

In global grasslands, the key environmental factors influencing GPP and ER response to grazing included WI, grazing duration, the response of soil moisture, and grazing intensity (Fig. S13; P < 0.05). For NEP, the major predictors were WI, grazing duration and intensity (P < 0.05). The most important environmental parameters for AGB response to grazing were grazing intensity, WI, grazing duration, the response of soil moisture and temperature and livestock types. For BGB, the major predictors were WI, grazing duration, the response of soil moisture and temperature. The response of NEP was positively correlated with WI under light, moderate and total grazing in global grasslands (Fig. 7; P<0.1). The response of ecosystem CO₂ fluxes was affected by AGB under grazing in global grasslands (Fig. S14).

Relationships of the responses of ecosystem CO₂ fluxes with potential regulating factors in global grasslands from the meta-analysis.

The marginal (m) and conditional (c) R² indicate the proportion of variance explained by the fixed effects and by both the fixed and random effects (study), respectively. The black solid lines and gray shaded areas in panels are the mean and 95% confidence interval of the slope, respectively. The sizes of points are proportional to their corresponding weights. RR, response ratio; overall, total grazing; LG, light grazing; MG, moderate grazing; HG, heavy grazing.

Discussion

4.1 The responses of ecosystem CO₂ fluxes and plant biomass to grazing and controlling factors

Grazing decreased the responses of ecosystem CO₂ fluxes and plant biomass in global grasslands, but only NEP was not significantly affected by grazing in our field experiment (Fig. 4). The lack of a significant response in NEP may be because the site in this field study was managed for year-round continuous low-intensity grazing (Liang et al., 2021). In addition, grazing affected GPP and ER at similar magnitudes, resulting in limited net changes in NEP. Similarly, the reason why the non-significant response of NEP appeared only under MG may be that moderate grazing affected GPP and ER at similar magnitudes (Fig. 5). However, the reduction in response of GPP was higher than that of ER in LG and HG, resulting in the decrease in NEP. Both the results of field experiment and global meta-analysis showed that light grazing did not reduce NEP under wetter conditions (Fig. 3B, 6B and 7H). Light grazing usually stimulates leaf regrowth following defoliation, and these new leaves often are more physiologically active than the older leaves that contribute much of leaf area in ungrazed treatment (Polley et al., 2008), which likely imply a stronger leaf photosynthesis and C sink (Reich et al., 2007). Considering factors such as different grassland types (Fig. S11), livestock grazing modes (Fig. S12), climatic conditions, and grazing durations, the future grazing studies on NEP should focus more on ANPP and RGR, and extrapolate the results cautiously.

Grazing stimulated the NEP under wet conditions, but depressed it under dry conditions, a pattern consistent across the typical steppe in our experiment and supported by our global meta-analysis (Figs. 3B and 7H). This finding aligned with the finding that the response of NEP increased with the increase in wetness index under grazing. This relationship could be explained by the significant positive correlation of NEP with AGB (Fig. S14). The linear regression analysis also showed that light grazing stimulated the responses of NEP with increasing wetness index. This relationship could be explained by the significant positive correlation between NEP and both ANPP and RGR (Fig. S4). Under lower wetness conditions (WI ≤ 30), light grazing reduced GPP and ER to a lesser extent than moderate and heavy grazing (Fig. 6B). Compared with higher grazing intensities, light grazing consumed less forage by livestock, thereby better mitigated the effect of lower moisture on GPP and ER. Moderate grazing decreased GPP and NEP under lower WI, but the responses of GPP and NEP to moderate grazing were similar under higher WI in global grasslands (Figs. 6 and 8), indicating that higher wetness offset the response of GPP and NEP to moderate grazing. This was likely because grazing-induced biomass reduction was less under wet condition in grasslands (Jin et al., 2023). However, not all grazing intensities aligned with the finding that the response of NEP increased with the increase in wetness index, likely due to severe loss of vegetation resilience under heavy grazing. Heavy grazing, even under higher wetness, reduced NEP and AGB in global grasslands (Fig. 2, 5 and 6C), resulting in a severe decline or even loss of their resilience, which could not be restored to control levels in long-term heavy grazing (Irob et al., 2023).

Schematic summary of the effects of grazing intensity on ecosystem carbon dioxide (CO₂) fluxes and biomass in global grasslands in the meta-analysis.

The black and blue arrows indicate negative effects on overall grazing and grazing intensities, respectively. Asterisks (*) indicate significant effects on variables at P < 0.05. GPP, gross primary productivity; ER, ecosystem respiration; NEP, net ecosystem productivity; LG, light grazing; MG, moderate grazing; HG, heavy grazing.

Both our experiment and meta-analysis showed that aboveground biomass, grazing intensity and wetness index were key factors regulating the responses of ecosystem CO₂ fluxes to grazing (Fig. S3, S8 and S9), with their responses intensifying with higher aboveground biomass and wetness index. Although grazing reduced C₃ plant biomass, it had no significant effects on C₄ plant biomass or the C₃:C₄ plant biomass ratio (Fig. S7). Furthermore, variation in plant community composition did not significantly explain the response of NEP to grazing (Fig. S8), indicating that the photosynthetic pathway composition of the plant community was not the primary driver mediating grazing effects on ecosystem carbon sequestration (Delgado-Balbuena et al., 2013). Grazing reduced aboveground biomass and plant respiration (Liu et al., 2020; Wang et al., 2025), thereby decreasing GPP and ER in both field experiment and meta-analysis (Fig. 4). Plants enhanced photosynthetic structures and physiological functions under grazing pressure. These adaptations improved CO₂ assimilation and allocated more carbohydrates to compensate for the loss of aboveground biomass, thereby achieving compensatory growth (Wang et al., 2020; Yu et al., 2025). The increase in GPP and NEP in LG in 2020 was due to the increased relative growth rate in LG than in CK (Fig. S5), suggesting that grazing stimulated greater plant compensatory growth in years with more precipitation (Li et al., 2025). Moreover, appropriate grazing intensity facilitated the removal of senescent plant tissues, leading to increased leaf light capture capability and enhanced photosynthetic capacity (Shen et al., 2019). The aboveground and belowground biomass, as well as plant root/ shoot ratios were influenced by grazing activities such as feeding, trampling, and excretion (Cao et al., 2024). In the field experiment and global meta-analysis of this study, plant biomass (both aboveground and belowground biomass) was lower under heavy grazing (Figs. 2 and 5). Under heavy grazing conditions, where soil nutrients were often depleted, plants tended to prioritize biomass allocation to roots to enhance nutrient acquisition and utilization (Liu et al., 2024). Moreover, heavy grazing can stimulate C allocation to roots (Ren et al., 2024), which explains why the root/ shoot ratio was higher under heavy grazing (Figs. 2 and 5). In the wetter years (2020 and 2021), the aboveground biomass was higher while the root/ shoot ratio was lower than in the drier years. Heavy grazing affected the allocation strategy of aboveground and belowground biomass by plants.

Our long-term experiment and global meta-analysis jointly provided complementary evidence for understanding the effect of grazing on the ecosystem CO₂ fluxes in grassland ecosystems. Long-term grazing field experiments revealed the mechanisms underlying the effects of grazing on ecosystem CO₂ fluxes. Global meta-analysis further clarified the universality of these response patterns across different climatic zones and grassland types. It should be noticed that both field experiment and meta-analysis consistently revealed that wetness was an important factor in regulating the ecosystem CO₂ fluxes in response to grazing. Under wetter conditions, light grazing promoted compensatory plant growth, enhanced leaf turnover and photosynthetic recovery capabilities, thereby maintaining or even enhancing the NEP (Morgan et al., 2016; Owensby et al., 2006). Conversely, under drier conditions or heavy grazing pressure, a decrease in aboveground biomass and weakened ecosystem resilience jointly limited the ecosystem carbon uptake capacity. These results suggested that wetness modulated the effects of grazing on NEP in grasslands. Moderate grazing may be sustainable under better water conditions, while heavy grazing may exceed the ecosystem resilience threshold even under relatively wet conditions, leading to sustained ecological degradation.

4.2 Limitations and implications for future study

Our results highlight that light grazing serves as a promising management strategy to promote ANPP and CO₂ sequestration. However, some aspects still need to be further explored. The assessment of ecosystem CO₂ fluxes was incomplete due to limited measurement time, area and sampling intervals. This may bias the representation of seasonal cycles and spatial heterogeneity, especially in the regions that were not monitored. Multiple time-point sampling should be adopted; the sampling frequency and points should also be increased in the future field study. Furthermore, key intermediate processes, such as soil nutrient availability, changes in community composition, and leaf photosynthetic physiological parameters, should also be investigated in the future. Our meta-analysis has limitations due to the relatively small sample size, which stems from the scarcity of studies across global grasslands exploring the effects of grazing intensity on ecosystem CO₂ fluxes. The data of ecosystem CO₂ fluxes from large-scale eddy covariance flux towers were not included in this meta-analysis, because there is an absence of a robust model to convert data between flux tower observations and chamber-based methods. In addition, future studies could conduct more experiments of ecosystem CO₂ fluxes in South America, Africa and Oceania, which have large amounts of grasslands for grazing, but were rarely investigated. Furthermore, more drought or wetness indices should be investigated in future studies of grazing effects on ecosystem CO₂ fluxes.

There are also limitations in evaluating the effects of grazing on grassland-livestock ecosystem CO₂ fluxes in this study. Specifically, carbon emissions derived from animal respiration were not included in this study. Therefore, the results of the field experiment and meta-analysis in this study should not be interpreted as a complete carbon budget assessment of the grassland-livestock ecosystem. Our primary objective was to evaluate the carbon exchange processes between vegetation and soil under grazing disturbance. Thus, the grazing effects reported in the field experiment and meta-analysis of this study were responses at the vegetation-soil ecosystem scale rather than the net carbon balance of the entire grazing system.

In previous studies, meta-analysis literatures of effects of grazing intensities on NEP were limited and not adequate. Regardless of these limitations, the results of our field experiment and meta-analysis have significant implications for future studies and grassland production. The primary factors regulating ecosystem CO₂ fluxes responses to grazing were identified as aboveground biomass, wetness index and grazing intensity. The NEP was positively affected by wetness index in LG, MG and HG. However, in both the typical steppe grazing experiment and the synthesis of global grasslands, the NEP resilience increased with higher wetness index specifically under light grazing, but not under heavy grazing. During wetter years, the NEP was even higher under LG compared to CK, whereas in drier years, no significant difference was observed between LG and CK (Fig. 2). This pattern aligns with the greater RGR and ANPP observed under elevated wetness index in light grazing treatment (Fig. S4). Globally, higher wetness index and aboveground biomass enhanced the response of NEP to grazing (Figs. 3, 7 and S14). However, even under higher wetness conditions, heavy grazing reduced NEP and AGB in global grasslands (Fig. 6), reflecting a severe decline, or even loss of resilience under long-term heavy grazing pressure. These results indicate that the negative impacts of long-term heavy grazing on NEP could not be offset by higher annual wetness index in global grasslands, underscoring the necessity of implementing strict grazing management measures. Additionally, moderate grazing under wetter years may be an appropriate management strategy to sustain grassland carbon balance while supporting livestock production.

Conclusion

In summary, the meta-analysis of this study presents the first comprehensive assessment of how annual wetness index affects the response of ecosystem CO₂ fluxes to grazing across global grasslands. Our results showed that wetness index and aboveground biomass were key factors regulating the responses of ecosystem CO₂ fluxes, and both exhibiting positive associations with these responses. Grazing reduced the response of GPP and ER in our field experiment of typical steppe and in the meta-analysis of global grasslands. Higher wetness indices offset the effect of moderate grazing rather than heavy grazing on NEP in global grasslands. These findings underscore the importance of developing region-specific grazing strategies guided by local wetness indices.

Data availability

Data will be available on request.

Acknowledgements

The authors would like to acknowledge funding from the National Natural Science Foundation of China (U22A20559, 32260289 and 32271656), the Inner Mongolian Key Research and Development and Achievement Transformation Plan Project (2025YFDZ0062), the Science and Technology Innovation Major Demonstration Project of Inner Mongolia (2024JBGS0007), the Natural Science Foundation of Inner Mongolia (2025MS03013 and 2025MS03119), the State Key Laboratory of Vegetation Structure, Function and Construction (VegLab2025002), and the Fundamental Research Funds for the Directly-Affiliated Universities of the Autonomous Region (2026JBKY044). We would also like to acknowledge the staff in the National Climate Observatory in Xilinhot of Inner Mongolia, China for their help in data collection.

Additional files

Supplementary materials.

Additional information

Funding

MOST | National Natural Science Foundation of China (NSFC) (U22A20559)

Zhiyong Li

MOST | National Natural Science Foundation of China (NSFC) (32260289)

Le Qi

MOST | National Natural Science Foundation of China (NSFC) (32271656)

Cunzhu Liang

Inner Mongolian Key Research and Development and Achievement Transformation Plan Project (2025YFDZ0062)

Le Qi

Science and Technology Innovation Major Demonstration Project of Inner Mongolia (2024JBGS0007)

Le Qi

Natural Science Foundation of Inner Mongolia (2025MS03013)

Zhiyong Li

Natural Science Foundation of Inner Mongolia (2025MS03119)

Le Qi

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Article and author information

Author information

Yueqiang Wu
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
- These authors contributed equally to this manuscript.
Le Qi
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
ORCID iD: 0000-0002-5173-7227
- These authors contributed equally to this manuscript.
Hao Li
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
- These authors contributed equally to this manuscript.
Jiguang Feng
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
Peng Zhou
State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
Hangyu Li
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
Xiaoyang Gao
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
Zhijie Wang
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
Shilin Cui
Xilinhot National Climate Observatory, Xilinhot, China, Xilinhot Field Research Station for Grassland Ecological Meteorology, China Meteorological Administration, Xilinhot, China
Ping Yin
Xilinhot National Climate Observatory, Xilinhot, China, Xilinhot Field Research Station for Grassland Ecological Meteorology, China Meteorological Administration, Xilinhot, China
Wenhong Ma
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
Cunzhu Liang
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
Zhiyong Li
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China
- For correspondence: zylee007@imu.edu.cn
Biao Zhu
Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, Inner Mongolia Key Laboratory of Grassland Ecology, and School of Ecology and Environment, Inner Mongolia University, Hohhot, China, State Key Laboratory of Vegetation Structure, Function and Construction (VegLab), Ministry of Education Key Laboratory of Earth Surface Processes, and College of Urban and Environmental Sciences, Peking University, Beijing, China
- For correspondence: biaozhu@pku.edu.cn

Author Notes

Competing interests: No competing interests declared

Version history

Preprint posted: January 29, 2026
Sent for peer review: February 19, 2026
Reviewed Preprint version 1: April 24, 2026
Reviewed Preprint version 2: October 1, 2026

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.110725. This DOI represents all versions, and will always resolve to the latest one.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Significance of findings

Strength of evidence

Abstract

Introduction

Conceptual illustration of how grazing with wetness fluctuation would affect net ecosystem productivity (A), and geographic (B) and climatic distributions of data (C) included in this meta-analysis.

Materials and methods

2.1. Experimental site

2.2. Experimental design

2.3. Measurement of ecosystem CO2 fluxes

2.4. Climate conditions and soil microclimate

2.5. Plant sampling and analyses

2.6. Data Analysis

2.7. Meta-analysis of grazing experiments in global grasslands

Results

3.1 Grazing intensity effects on ecosystem CO2 fluxes and plant biomass in the field experiment

Effects of different grazing intensities on ecosystem carbon dioxide (CO2) fluxes and biomass in our field experiment.

Relationships of the responses of gross primary productivity (GPP) and net ecosystem productivity (NEP) with potential regulating factors from 2019 to 2023 under different grazing intensity.

3.2 Synthesis of grazing on ecosystem CO2 fluxes and plant biomass across global grasslands

Effects of grazing on gross primary productivity, ecosystem respiration, net ecosystem productivity, aboveground biomass, belowground biomass and root shoot ratio, soil moisture and soil temperature, in global grasslands in the field experiment in this study (A) and from the meta-analysis (B).

Effects of different grazing intensities on ecosystem CO2 fluxes, biomass and soil temperature and moisture in global grasslands from the meta-analysis.

Responses of ecosystem CO2 fluxes and biomass to grazing (A) and different grazing intensities (B-C), at wetness index ≤ 30 and wetness index > 30 in global grasslands from the meta-analysis.

Relationships of the responses of ecosystem CO2 fluxes with potential regulating factors in global grasslands from the meta-analysis.

Discussion

4.1 The responses of ecosystem CO2 fluxes and plant biomass to grazing and controlling factors

Schematic summary of the effects of grazing intensity on ecosystem carbon dioxide (CO2) fluxes and biomass in global grasslands in the meta-analysis.

4.2 Limitations and implications for future study

Conclusion

Data availability

Acknowledgements

Additional files

Additional information

Funding

References

Article and author information

Author information

Yueqiang Wu#

Le Qi#

Hao Li#

Jiguang Feng

Peng Zhou

Hangyu Li

Xiaoyang Gao

Zhijie Wang

Shilin Cui

Ping Yin

Wenhong Ma

Cunzhu Liang

Zhiyong Li

Biao Zhu

Author Notes

Version history

Cite all versions

Copyright

Metrics

2.3. Measurement of ecosystem CO₂ fluxes

3.1 Grazing intensity effects on ecosystem CO₂ fluxes and plant biomass in the field experiment

Effects of different grazing intensities on ecosystem carbon dioxide (CO₂) fluxes and biomass in our field experiment.

3.2 Synthesis of grazing on ecosystem CO₂ fluxes and plant biomass across global grasslands

Effects of different grazing intensities on ecosystem CO₂ fluxes, biomass and soil temperature and moisture in global grasslands from the meta-analysis.

Responses of ecosystem CO₂ fluxes and biomass to grazing (A) and different grazing intensities (B-C), at wetness index ≤ 30 and wetness index > 30 in global grasslands from the meta-analysis.

Relationships of the responses of ecosystem CO₂ fluxes with potential regulating factors in global grasslands from the meta-analysis.

4.1 The responses of ecosystem CO₂ fluxes and plant biomass to grazing and controlling factors

Schematic summary of the effects of grazing intensity on ecosystem carbon dioxide (CO₂) fluxes and biomass in global grasslands in the meta-analysis.

Yueqiang Wu

Le Qi

Hao Li