estimating the soil carbon sequestration potential of china's grain for green project

Estimating the soil carbon sequestration potentialof Chinas Grain for Green ProjectShengwei Shi1,2,3 and Pengfei Han1,2

1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of AtmosphericPhysics, Chinese Academy of Sciences, Beijing, China, 2University of Chinese Academy of Sciences, Beijing, China, 3Collegeof Forestry, Northwest A&F University, Shaanxi, China

Abstract The largest area of planted forest in the world has been established in China throughimplementation of key forestry projects in recent years. These projects have played an important rolein sequestering CO2 from the atmosphere, which is considered to be a potential mitigation strategy for theeffects of global climate change. However, carbon sequestration in soil (soil organic carbon, SOC) afterafforestation or reforestation is not well understood, particularly for specific key forestry projects. In this study,the SOC change in the top 20 cm of soil for each type of restoration implemented under Chinas Grainfor Green Project (GGP) was quantified by a meta-analysis of data from published literature and direct fieldmeasurements. Soil carbon sequestration due to the GGP during 19992012 was estimated using data onthe annual restoration area at provincial level and functions that relate SOC stock change to controllingfactors (e.g., plantation age, forest zone, and type of forestation). Soil carbon sequestration of the GGP wasestimated to be 156108 Tg C (95% confidence interval) for current restoration areas prior to 2013, with amean accumulation rate of 128 Tg C yr1. The soil carbon sequestration potential of existing plantationzones is predicted to increase from 156108 Tg C in 2013 to 383188 Tg C in 2050 under the assumptionthat all plantation areas are well preserved. Plantations in northwestern, southern, and southwesternzones contributed nearly 80% of total soil carbon sequestration, while soil C sequestration in northeasternChina was much more variable. Improved data sources, measurements of SOC in the organic layer,greater sampling depth, and better distribution of sampling sites among GGP regions will reduce theuncertainty of the estimates made by this study.

1. Introduction

Forests have a very important role in the terrestrial carbon (C) cycle and sustainable human development[Pan et al., 2011]. The largest area of planted forest in the world was established in China by theimplementation of six key forestry projects during past three decades [Fang et al., 2010]. The six key forestryprojects, including the Natural Forest Protection Program, Grain for Green Project (GGP), Three-NorthsProject, Fast-Growing and High-Yielding Timber Base Construction Program Project, and the Beijing-TianjinSandstorm Source Control Project, have contributed more than 90% of the increase in total plantedforest area in China during 19992012 [State Forestry Administration (SFA), 2013]. The C storage of forestbiomass and soil in China increased by one third due to these large-scale forestation (including afforestationand reforestation) efforts [Pan et al., 2011]. Current estimates of the forest C budget have mainly focusedon C sequestered in biomass [Persson et al., 2013; Xu et al., 2010], while the magnitude of soil C sequestrationin the terrestrial C cycle remains more difficult to quantify [Mckinley et al., 2011; Schulze et al., 2010].An increase in soil C sequestration is a cost-effective, long-term and beneficial process for soil fertility[Lal, 2004; Li et al., 2012]. But it is difficult to detect the increment in SOC stocks after afforestation orreforestation to a high degree of precision due to the great spatial heterogeneity of soil C change in forestecosystems [Conant et al., 2003; Hoover et al., 2003]. Furthermore, the mechanisms of SOC change followingafforestation or reforestation are much more complex than those for C sequestered in biomass [Laganireet al., 2010]. Process-based models can assist in simulating the dynamics of C storage in abovegroundbiomass and soil at a specific forested site [Kirschbaum et al., 2008], but estimating C sequestration at regionalor national scales is still a challenge.

GGP is a systematic forestation campaign in China, mainly consisting of four parts: forestation on cropland,forestation on barren land, fostering forest by natural succession on abandoned cropland and barren

SHI AND HAN 2014. American Geophysical Union. All Rights Reserved. 1

PUBLICATIONSGlobal Biogeochemical Cycles

RESEARCH ARTICLE10.1002/2014GB004924

Key Points: Soil C sequestration of the GGP was156 Tg C prior to 2013

Soil C sequestration is an importantpart of the terrestrial carbon sink

Supporting Information: Readme Text S1 Text S2 Text S3 Table S1 Table S2 Table S3 Table S4

Correspondence to:S. Shi,[email protected]

Citation:Shi, S., and P. Han (2014), Estimatingthe soil carbon sequestration potentialof Chinas Grain for Green Project,Global Biogeochem. Cycles, 28,doi:10.1002/2014GB004924.

Received 25 JUN 2014Accepted 14 OCT 2014Accepted article online 17 OCT 2014

http://publications.agu.org/journals/http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1944-9224http://dx.doi.org/10.1002/2014GB004924http://dx.doi.org/10.1002/2014GB004924

land, and conversion from cropland to grassland [SFA, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,2008, 2009, 2010, 2011, 2012, 2013]. Forestation on cropland and barren land establishes forests ondegraded landscapes for the purpose of reducing surface runoff and soil erosion and improving soilfertility [Chang et al., 2012]. Natural succession on abandoned cropland and barren land is accomplished byprotecting the damaged landscape from human disturbance by fencing, then forming forest ecosystems byrelying on natural succession over long time scales [Zhang et al., 2010]. Conversion from cropland to grasslandis a particular form of restoration within the GGP, which increases perennial plant coverage in areas withwater shortages [Chen et al., 2007]. By the end of 2012, as much as 27.2Mha area had been restored underthe GGP, of which 33% involved forestation on cropland, 56% involved forestation on barren land, 10%involved natural succession, and 1% adopted conversion from cropland to grassland [SFA, 2013]. There hasbeen considerable interest from scientists in soil C change after adoption of these treatments under theGGP, and since the 1990s, hundreds of papers have been published reporting field observations of SOCchange after afforestation or reforestation at the local scale [Chang et al., 2012; Wei et al., 2012]. Thesestudies have shed light on the mechanisms of SOC dynamics and provided evidence for estimation of soil Csequestration attributable to the GGP. In recent years, the magnitude and potential of SOC sequestrationassociated with the GGP at national scale has been quantitatively synthesized using different approaches[Zhang et al., 2010; Zhao et al., 2013; Deng et al., 2013; Song et al., 2014]. It has been estimated that the SOCaccumulated after conversion of cropland to forest averaged 37 g C m2 yr1 (time-weighted mean) in thetop 20 cm of soil [Zhang et al., 2010], which is very similar to the 33 g C m2 yr1 (linear regression slope ofplantation age and SOC stock change) reported by Deng et al. [2013] but much lower than the 54 g Cm2 yr1 (time-weighted mean) reported by Zhao et al. [2013]. Song et al. [2014] also reported results of ameta-analysis of change in the SOC stock after restoration of cropland, estimating that SOC stock increasedby 48% in the top 20 cm. Based on the estimates of mean annual rates of SOC accumulation afterrestoration, the total SOC sequestration of the GGP has been estimated to be 11.7 Tg C yr1 (based on atotal GGP area of 32 Mha) [Zhang et al., 2010], or 14.5 Tg C yr1 (based on a total GGP area of 26.8 Mha)[Zhao et al., 2013]. These studies have also assessed SOC change rates or SOC stock changes under differentplant species (conifer, broadleaf, evergreen, and deciduous) [Deng et al., 2013], land-use types (e.g., forest,shrub, and grassland) [Song et al., 2014; Zhao et al., 2013], restoration ages [Zhang et al., 2010; Deng et al.,2013; Song et al., 2014], and climatic zones [Zhao et al., 2013] at national scale, which has greatly increased theknowledge of SOC sequestration associated with the GGP in China.

However, the previous reviews had some shortcomings in terms of data collection, analysis, and predictionof SOC sequestration under the GGP. First, the response of SOC stocks to forestation is strongly influencedby land-use type prior to forestation [Laganire et al., 2010; Li et al., 2012; Shi et al., 2013; Brcena et al.,2014]. The existing quantitative reviews only focused on SOC change after restoration on cropland[Zhang et al., 2010; Deng et al., 2013; Song et al., 2014] and neglected SOC dynamics after restoration onbarren land or in forests formed through natural succession. Due to the high proportion of forestationon barren land in the total land area of GGP (56%) [SFA, 2013], ignoring SOC sequestration after restorationon barren land or forests formed through natural succession is a serious omission. Second, as plantationsage, the response of SOC stocks to restoration is nonlinear [Silver et al., 2000; Zhang et al., 2010]. Theexisting reviews also demonstrated that there was great variation in SOC change rates at different plantationstages, and that there is a high risk of soil C losses in young stands [Paul et al., 2002; Zhang et al., 2010;Deng et al., 2013]. Furthermore, the response of SOC to forestation is directly influenced by climatic zoneand type of forestation [Laganire et al., 2010; Li et al., 2012; Jin et al., 2014]. Thus, it is unreasonable toestimate the SOC sequestration of the GGP using multiplication of a constant mean SOC change rate by thetotal restoration area.

In this study, the soil C sequestration potential of GGP is assessed based on two factors. First, of the six keyforestry projects in China, only the annual plantation areas of GGP are recorded according to prior landuse (cropland or barren land) and types of restoration (planted forest, forest formed through naturalsuccession, and perennial grassland established on cropland) [SFA, 2000, 2001, 2002, 2003, 2004, 2005,2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013]. Second, most experiments in situ have focused on SOCchange after forestation on cropland, forestation on barren land, natural succession on abandoned cropland,or conversion cropland to grassland [Chen et al., 2007; Zhang et al., 2010]. In contrast, information aboutsoil C sequestration in natural forest or shelterbelt forests established through other key forestry projects is

Global Biogeochemical Cycles 10.1002/2014GB004924


lacking. Therefore, if a relationship between SOC dynamics and plantation age in each climate zoneand type of restoration can be established, then it is feasible to estimate the soil C sequestration potentialof the GGP.

Here we combine a literature data set of SOC change after restoration (a total of 211 publications) and anextensive field experiment using the paired-plot method on the Qinghai Plateau in China. Our objectiveswere to (1) estimate the soil C sequestration potential of the GGP at a national scale and (2) predict thispotential and discuss its implications and uncertainties.

2. Materials and Methods2.1. Data Source

Literature searches were performed using online databases in the Chinese Academy of Sciences, withsearch topics related to SOC change occurring in croplands and barren lands caused by restoration inChina (including afforestation, reforestation, grass plantation, and natural succession) (http://www.isiknowledge.com/ and http://www.cnki.net/). We collected more than 400 articles including journalpapers or dissertations in either English or Chinese. In accordance with previous meta-analyses [Laganireet al., 2010; Don et al., 2011], SOC change after restoration was analyzed using three different approaches,including chronosequence, paired sites, and retrospective design. There was very little data from long-term experimental sites using a retrospective design (>10 year) to estimate forest soil C change in China. Inchronosequence or paired site studies, the SOC stock in adjacent croplands that were free of barnyardmanure was considered to be reference values for restored plots [Shi et al., 2013].

In order to reduce publication biases, literature was selected according to the following four criteria: (1) theSOC stock or concentration of prerestoration and postrestoration must have been assessed; (2) the samestratified method for soil sampling must have been employed for restored and reference plots, and samplesmust be collected to a depth of at least 10 cm; (3) plantation age and dominant species of restored plots musthave been reported; and (4) when the chronosequence approach was employed, the soil type must beconsistent and stand agemust be no less than 3 years. For studies using the chronosequence approach with along time series, the plot with the lowest plantation age (15 years) was taken as the reference value ifthere was no reported reliable reference plot for a plantation age of zero [Nave et al., 2010]. Numerical valueswere extracted from digital figures using GetData Graph Digitizer. On this basis, a preliminary data set on SOCresponse to restoration was compiled from 211 published studies from 1993 to 2013, including detailedinformation on site location, climate factors, soil properties, experimental design, soil sampling depths, andnumbers of repeated samples. The data set is available in Text S1 and Table S1 in the supporting information.

In addition, extensive field experiments using paired-plot methods were performed during JuneSeptember2011 on the Qinghai Plateau to augment the sample size. Sample sites included 42 sites of forestation oncropland, 6 sites of forestation on barren land, and 10 sites of conversion from cropland to grassland[Shi, 2013]. More detailed information about field experiments using paired-plot methods on the QinghaiPlateau is described in Text S2 and Table S2.

Finally, a complete data set was established combining the results from the literature survey and fieldexperiments. The measurement sites are shown in Figure 1. We subdivided the data set into two subdata setsaccording to whether the studies reported SOC concentration or BD: subdata set I includes data pointswhere both SOC concentration and BD were available in the reference, and subdata set II includes data pointswhere only SOC concentration or stock (SOC concentration and BD) was reported. The values in data set I arealso part of data set II. The values in the two subdata sets were both coded according to prior land useand types of restoration: forestation on cropland, forestation on barren land, natural succession onabandoned cropland and barren land, and conversion from cropland to grassland.

2.2. Restoration Area and Zoning in China

The area of annual restoration under the GGP from 19992012 in each province is recorded in the ForestryStatistics Yearbook of China [SFA, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,2012, 2013], which is the only authorized data published by the State Forestry Administration. Forestryzoning is based on comprehensive analysis of climate, plant species, soil and geographic factors, andsocio-economic development levels [National Commission on Agricultural Resources, 1989]. For the current



http://www.isiknowledge.com/http://www.isiknowledge.com/http://www.cnki.net/

study, an optimal geographic forestation zoning for the GGP was imposed as follows: (1) Northeasternregion (FZ1, including the provinces of Liaoning, Jilin, and Heilongjiang), (2) Northwestern region (FZ2,including the provinces of Inner Mongolian, Gansu, Qinghai, Ningxia, and Xinjiang), (3) the Loess Plateauand the North China Plain (FZ3, including the provinces of Hebei, Henan, Shandong, Shanxi, and Shaanxi),(4) Southern region (FZ4, including the provinces of Anhui, Fujian, Hainan, Hubei, Hunan, Guangdong,Guangxi, Guizhou, Jiangxi, Jiangsu, and Zhejiang), and (5) Southwestern region (FZ5, including the provinces ofChongqing, Sichuan, Yunnan, and Tibet). We did not subdivide the data on conversion from cropland tograssland due to the small sample size at the national scale.

2.3. Main Calculations

The SOC content in a fixed layer i was calculated using equation (1) [Yang et al., 2007],

Ci SOCi BDi hi 101 (1)

Ct Xn

i1Ci (2)

where Ci and SOCi are SOC density (Mg C ha1) and concentration (g kg1) for i sampling layer, respectively;

BDi is the bulk density (g cm3), and hi is the thickness of the soil layer (cm). If a study reported soil

organic matter, SOCi was calculated using a coefficient of 0.50 [Pribyl, 2010]; Ct is the sum of SOC contentacross all layers of the soil profile.

Soil BD estimates are critical for calculations of Ci, but many studies did not measure this attribute, particularlyin subdata set II. We established an empirical relationship between soil BD and SOC concentration with

Figure 1. Distribution of soil sampling sites after restoration (FC, FB, NS, and CG represent forestation on cropland, foresta-tion on barren land, natural succession on abandoned cropland, and barren land and conversion from cropland tograssland, respectively) in China. The different background shadings with FZ1, FZ2, FZ3, FZ4, and FZ5 refer to geographicforest zone of northeastern region, northwestern region, the Loess Plateau and the North China Plain, southern region,and southwestern region. See Tables S1 and S2 for details.



the reported values for each restoration modelfrom the subdata set I (Figure 2). Then, the missingvalues of soil BD were interpolated using thepredicted values from the empirical functionsin Figure 2.

Due to spatial and temporal variation in soil BD,the SOC stock calculated on a fixed depth basis(FD) will introduce bias into estimates of changesin SOC stock [Ellert and Bettany, 1995; Lee et al.,2009]. The equivalent soil mass (ESM) method,which assumes SOC stocks are based on anequivalent mass, has proven to be more reliablethan the fixed depth based method whenevaluating changes in SOC stock with landuse [Ellert and Bettany, 1995; Lee et al., 2009].Therefore, the value of SOC stocks after restorationwas corrected based on equivalent soil mass usingthe equations reported by Poeplau et al. [2011].

Soil sample depth varied among the studiesbetween 10 and 30 cm. In order to makecomparable estimates of SOC change to the samesample depth and to include more experimentalresults from some particular regions (e.g., thenumber of data points is much lower in thenortheastern and southwestern regions than inother regions), the SOC storage changes withirregular sample depths (h) were adjusted tothose of the top 20 cm of the sample usingthe equation (3).

C20 Ch (3)where C20 is the expected SOC density adjusted tothe top 20 cm soil layer at a specific site, Ch ismeasured SOC density at sample depth h (cm) ata specific site, and is a coefficient for the

adjustment of SOC density at h depth to the top 20 cm of corresponding soil types, which was derived basedon a study by Yang et al. [2007]. The derivation of for a given soil type is provided in Text S3 in thesupporting information.

SOC stock change (Cj, Mg ha1) in the top 20 cm depth was used to evaluate the SOC change after

restoration. Each observation of SOC stock change (Cj , Mg ha1) for j site was calculated with formula (4)

[Shi et al., 2013].

Cj Cej Ccj (4)

Cej is the SOC stock of forested sites (Mgha1), and Ccj is the SOC stock of the j observation for reference

sites (Mgha1).

We used two indices to describe the degree of change in SOC after restoration: relative change in SOC stock(Yj, %) and mean annual absolute rate of change in SOC stock (Aj , Mg ha

1 yr1). The Yj and Aj at a singlesite were calculated using formula (5) [Laganire et al., 2010] and formula (6) [Li et al., 2012], respectively.

Yj Cej=Ccj (5)Aj Cj=Age (6)

where Age is the duration after restoration.

Figure 2. Empirical functions for estimating the missing soilBD based on data from studies reporting SOC concentrationand soil BD. (a) Land-use change of cropland, including for-estation on cropland, natural succession on abandonedcropland or conversion of cropland to grassland, and (b) Land-use change of barren land, including forestation on barrenland, and natural succession on barren land. The solid linesrepresent the optimum fitted function with the maximum R2,and the dashed lines represent their 95% confidence bands.



2.4. Meta-Analysis

In order to include as many studies as possible, we performed analyses using nonparametric weightingfunctions. The natural log of the response ratio (R) was employed as the effect size of the response variables(soil BD, SOC concentration, and stock) after restoration for each individual study in subdata set I usingequation (7) [Hedges et al., 1999].

ln Rj ln Xej=Xcj ln Xej

ln Xcj

(7)

where Xej is the mean of each variable in an afforested plot and Xcj is the mean of the correspondingvariable in the reference plot at jth site.

The weighting function of individual studies was calculated by replication using equation (8) [Adams et al., 1997].

wj ncj nejncj nej (8)

where ncj and nei are the replication in reference and forested plots at jth site. Then, mean effect sizes acrossmultiple sites were estimated using equation (9) [van Groenigen et al., 2013].

ln R X

ln RjwjXwj

(9)

We used METAWIN 2.1 to calculate the mean effect size, and 95% confidence intervals (CI) were generatedusing a bootstrapping procedure (4999 iterations) [Rosenberg et al., 2000; van Groenigen et al., 2013]. Therelative change percentage of each response variable was back transformed from the effect size using afunction of ([R 1] 100%) [van Groenigen et al., 2013].For data set II, the mean effect size was also calculated using a similar method and METAWIN 2.1 to estimateannual absolute rate of change in SOC stock (Mgha1 yr1) under different restoration types and ineach region.

2.5. Modeling of the Response of SOC Change After Restoration

A linear mixed model was used to model the response of SOC changes after forestation at a national scale,where the relative change in SOC stocks (%) was the response variable, the fixed explanatory variablesconsisted of plantation age, geographic forestation zone, type of forestation, and study approach, and therandom variables included either sample site or corresponding study authors. However, the relativechange values for SOC stocks in data set II did not fit a normal distribution. Since many values of relativechange in SOC stock were negative, we transformed all the values of relative change in SOC stock to the formln(Y+ Y0) before input into a model, where Y0 is a constant, which we set at 60 because the minimum relativechange in SOC stock was 58.6%. After this transformation, the data fit a normal distribution. Previousstudies found that logarithmic functions were appropriate to fit soil C sequestration and plantation agedata [Silver et al., 2000; Zhang et al., 2010], so the response of SOC change was analyzed using the followinglinear model (10):

ln Ys c a ln Age FZm FMn SAP Imn Inp Rsite (10)

where Ys is a substitution of (Y+ Y0), c is a constant, Age is the duration after forestation, a is the effects of age,FZm is the effect of geographic forestation zone (m is FZ1, FZ2, FZ3, FZ4, and FZ5), FMn is the effect of typeof forestation (n is forestation on cropland, forestation on barren land, and fostering forest by naturalsuccession), SAp is the effect of study approach (p is chronosequence, paired sites, and retrospective design),(Imn Inp) is an abbreviation of the effect of first-order interactions from the four controlling factors(plantation age, geographic forestation zone, type of forestation, and study approach), Rsite is the randomeffect due to sample site or corresponding study authors, which accounts for individual differences in theexperimental data, and is the residual error.

The effects of the fixed and random variables on the response of SOC change after forestation were computedby fitting equation (10) to observations using the Statistical Analysis System (SAS) procedure MIXED (Release9.1, SAS Institute Inc., Cary, NC, USA, 2004). As all studies were not of the same quality (e.g., some studies had



few replications in forested and reference plots), we weighted the analysis using the weighting function ofreplications (wj) (WEIGHT statement). The significance of the effect of fixed factors (including their first-orderinteractions) was tested using a form of analysis of variance. When the effects of fixed variables were significant,

their parameters were estimated forthe linear mixed model.

The response of SOC change afterconversion from cropland to grasslandwas fitted using a logistic growthmodel at national scale as shown inequation (11) [Xu et al., 2010],

Yg W1 k etAge (11)where W, k, and t are constants, Yg isthe relative change of SOC stockafter conversion cropland to grassland,and is a random error factor.

The soil C sequestration after forestationwas summarized as formula (12)[Yu et al., 2009]

Cmn Ccmn Ymn (12)where Cmn is the increase of SOC stock(Mg C ha1) in the mth geographic

Table 1. Mean SOC Stocks in Reference Plots and Mean SOC Change Rates at Different Forestation Zonesa

Restoration Type

SOC Stocksin Reference(Mg C ha1)b

Absolute SOCChange Rate

(Mg C ha1 yr1)c

Relative SOCChange Rate(% yr1)d

Mean TimeSpan (year) kForestation Zone Mean 95% CI Mean 95% CI Mean 1SE

Forestation on Cropland (FC)Northeastern (FZ1) 38.45 27.64 to 51.56 0.05 0.24 to 0.18 ns 18.0 22Northwestern (FZ2) 22.80 20.04 to 25.67 0.51 0.29 to 0. 73 4.66 0.68** 15.6 90Loess Plateau and North Plain (FZ3) 14.29 10.92 to 18.66 0.25 0.19 to 0.31 0.77 0.08** 25.1 117Southern (FZ4) 29.71 28.40 to 31.04 0. 88 0.47 to 1.27 2.20 0.65** 11.3 68Southwestern (FZ5) 28.94 25.55 to 32.36 0.57 0.26 to 0.87 3.01 0.99** 9.4 100

Forestation on Barren Land (FB)Northeastern (FZ1) 43.71 30.81 to 56.74 0.07 0.23 to 0.08 ns 20.7 24Northwestern (FZ2) 8.71 6.99 to 10.60 0.43 0.25 to 0.61 3.17 0.77** 16.4 76Loess Plateau and North Plain (FZ3) 14.23 11.31 to 17.48 0.34 0.24 to 0.44 0.76 0.63** 20.4 54Southern (FZ4) 18.55 16.00 to 21.43 0.64 0.50 to 1.20 2.36 0.88** 23.0 82Southwestern (FZ5) 21.84 11.57 to 33.87 0.86 0.51 to 1.18 4.99 5.27** 10.9 18

Natural Succession (NS)Northeastern (FZ1) 42.88 31.11 to 54.67 1.54 0.64 to 2.46 8.75 3.52** 8.8 9Northwestern (FZ2) 22.08 19.47 to 24.86 0.70 0.50 to 0.92 1.17 0.25** 23.3 62Loess Plateau and North Plain (FZ3) 12.12 9.74 to 15.03 0.19 0.06 to 0.31 1.17 0.17** 24.0 134Southern (FZ4) 40.11 30.86 to 49.59 1.24 0.66 to 1.94 1.96 0.25** 22.3 18Southwestern (FZ5) 31.19 21.33 to 40.46 0.97 0.46 to 1.59 2.47 0.81* 19.5 9Conversion cropland to grassland (CG) 17.10 13.80 to 20.97 0.59 0.19 to 1.01 10.65 1.22** 7.3 58

ans: nonsignificant regression model for observations (p > 0.05); SE: standard error.bWithin 020 cm, which is calculated by a weighted meta-analysis.cCalculated by a weighted meta-analysis. If 95% CI did not overlap zero, the absolute change rate in SOC stock was considered to be significantly different

from 0 (p < 0.05).dCalculated using a geometric growth model of y = A (1 + r)Age, where r is relative annual SOC change rate, * and ** represent the significance of the regres-

sion model at 0.05 and 0.01 level, respectively.

Figure 3. Mean relative changes (%) in SOC concentration, BD, SOC stockcalculated on a fixed depth basis (FD), and SOC stock in equivalent soilmass (ESM) after restoration for studies reporting SOC concentrationand BD using a meta-analysis (values in parentheses represent the numberof input data, and error bars indicate the 95% confidence interval).Abbreviations as shown in Table 1.



forestation zone and nthtype of forestation, C

cmn is

the corresponding SOC stock(Mg C ha1) before restoration(Baseline), and Ymn is the predictedvalue of relative change in SOCstock in the mth geographicforestation zone and nth type offorestation as estimated by thefixed effects parameters in thefitted models. The random effects(Rsite), which represent the generalvariability among sample sites[Schneider, 2013], were assigned tobe 0 when applying the linearmixed model to estimate relativechanges in SOC stocks afterforestation at a regional scale. The95% confidence interval of Ymn wasestimated using the equationsreported by Wroughton [2007].

Table 2. Results of the Linear Mixed Model Developed to Identify Randomand Selected Fixed Effects on the Response of SOC Stock to Forestationin China

Covariable Estimate Standard Error PZa

Sampling sites 0.0745 0.0107

2.6. Estimation of Total Soil CSequestration Potential

The total soil C sequestration potential for theGGP from 1999 to 2012 was estimated based onthe above procedures. Since it is very difficult toassess the baseline of each geographicforestation zone and type of restoration, weassumed that themean SOC stocks (Mg C ha1) inreference plots were sufficient to approximatethe value of the baseline before restoration(Table 1). The increment of soil C sequestrationcan then be obtained using the predicted valuefrom fitted models for each restoration areaand zone. The total increment of soil Csequestration in forest land under the GGPduring 19992012 was calculated as the sum ofall the types of restoration and zones usingformula (13). Similarly, the future soil Csequestration potential in 20202050 wasalso estimated based on fitted models of (10)and (11), assuming all the plantation areas arekept well conserved.

CT X5

m1

X3

n1Amn Cmn (13)

where, CT is total soil C sequestration potentialafter forestation for GGP, and Amn is conversionarea (ha) in the mth forestry zone using the nthtype of forestation.

3. Results3.1. Change in Soil BD, SOC Concentration,and Stock After Restoration

The results of meta-analysis from values in dataset I indicate that all types of restoration appliedin the GGP can increase SOC concentrationsignificantly, with a range from conversion ofcropland to grassland (+12.4%) to forestation onbarren land (+87.5%), while the soil BD wasreduced after conversion of cropland to grassland(0.5%), forestation on cropland (4.5%),natural succession (8.8%), and forestation onbarren land (10.8%) (Figure 3). The SOC contentin the top 20 cm was increased by 45.7%based on fixed depth method, and by 51.9%

based on equivalent soil mass method (Figure 3). Without soil mass correction, SOC sequestration afterrestoration would be underestimated by 6.2%.

3.2. Rate of Change in SOC Stocks After Restoration

The mean annual absolute change rate in SOC stock in each type of restoration varied greatly amongdifferent forest zones (Table 1). In all regions except for the northeastern region, the mean annual absolutechange rate of SOC stock after restoration was significantly higher than zero (p < 0.05). The mean SOC stock

Figure 4. Comparison of simulated and observed relative SOCchange (%) of different types of restoration according to linearmixed model. (a) Forestation on cropland, (b) forestation onbarren land, and (c) natural succession of abandoned croplandand barren land. The solid lines represent the fitted line, andthe dashed lines represent the 1:1 line.



in reference plots in the northeasternregion was higher than in otherregions. In addition, the meanrate of change in SOC stock afterforestation in the northeastern regionvaried substantially, ranging from0.07Mg C ha1 yr1 for forestation onbarren land to 1.54Mg C ha1 yr1 fornatural succession. The mean annualchange rate in SOC stock in thenorthwestern region and the LoessPlateau and North China Plain zone wassimilar for planted forest established oncropland and barren land, but therewas a broader range for the naturalsuccession (0.190.70Mg C ha1 yr1).The southern and southwesternregions had a similar rate of change(0.570.97Mg C ha1 yr1) for

forestation on cropland and barren land. Over a 7 year span, SOC sequestration after conversion fromcropland to grassland was 0.59Mg C ha1 yr1 across China (Table 1).

The mean annual relative change rates in SOC stock in each type of restoration also varied greatly betweenforest zones (Table 1), with a range between 0.77% yr1 under forestation on cropland in the LoessPlateau and North China Plain region to 8.75% yr1 under the natural succession in the northeastern region.Conversion of cropland to grassland has the highest mean annual relative change rate in SOC stock at10.65% yr1. The values of mean annual relative change rate in SOC stock indicated that there was apronounced positive impact on the soil C sequestration under the GGP, except for soils under planted forestsin the northeastern region.

3.3. Model Parameter Estimation

Based on estimates of the models covariance parameters, sample site is a significant explanatory variable forSOC change after forestation in our data set (Table 2). After excluding the effects of less important factors inthe full linear mixed model (Table S3), the selected fixed variables (including plantation age, forestryzone, type of forestation, and two interactions) significantly affected the response of SOC stocks (Table 2).The F value of plantation age is the biggest, indicating that plantation age is the predominant factorcontrolling SOC stock change after forestation. Although other variables (including forestry zone, type offorestation, and their interaction with plantation age) had a significant influence on the response of SOCstocks after forestation, their contribution to the variance was minor in this model.

The estimated effects of the selected variables with 95% confidence interval are shown in Table 3. The SOCstock after forestation increased with plantation age under three types of forestation except in thenortheastern region. Among all forest zones, the northwestern region had the lowest SOC accumulation atthe initial restoration stage (the smallest intercept in the linear model), but it has the highest SOCaccumulation rate with increasing plantation age (the biggest slope between the response of the SOC stockand plantation age). SOC accumulation under natural succession and forestation on barren land was ingeneral higher than that under forestation on cropland, particularly for the young stands.

According to the estimated effects and variables, a comparison between simulated and observed SOC stockchanges for each type of forestation was performed (Figure 4). The model established in this study,in principle, underestimated SOC sequestration when the relative change in SOC stock >100%, andoverestimated SOC change when SOC loss occurred. The selected variables can explain up to 31%, 23%, and32% of the variability in SOC change after forestation on cropland, forestation on barren land, and naturalsuccession (Figure 4), respectively.

The logistic nonlinear regression fitted well the relative change in SOC stock after conversion fromcropland to grassland (R2 = 0.64) (Figure 5).

Figure 5. SOC dynamics after conversion from cropland to grassland inChina. The solid lines represent the optimum fitted function with themaximum R2, and the dashed lines represent 95% confidence bands.



3.4. The Total Soil C SequestrationPotential Under the GGP

Soil C sequestration in each type ofrestoration of the GGP was estimatedaccording to the above linear mixedmodel for forest and the logistic modelfor grassland with the correspondingplantation areas obtained from theForestry Statistics Yearbook. Theresults showed that there is a greatC sequestration potential afterrestoration in each forest zone in China,except for forestation in the northeasternregion (Figure 6). In total, the soilC sequestration of the GGP was156108 Tg C (95% confidence interval)for the existing plantation area as of2013, with a mean accumulation rate of128 Tg C yr1. During 1999 to 2012, thesoil sequestered in through forestationon cropland and barren land contributedas much as 90% of the total soil Csequestration of the GGP (Figure 7). Incontrast, the soil sequestered due toforestation by natural succession andconversion of cropland to grassland onlycontributed

4. Discussion4.1. Uncertainties

Many factors, including data sources, soilsampling methods, and lack of a truebaseline, have introduced uncertaintyinto the results. The main potential sourcesof uncertainty are discussed below.

1. Data sources. The data set of SOCsequestration after restoration wascombined from field experiments and aliterature survey. Most sampling sitesemployed nonretrospective methods(chronosequence and paired-plotmethod), and these methods introducea great bias into the estimate of SOCchange [Hoover et al., 2003]. Due to thelong periods over which forests grow,

there is a lack of sufficient sampling sites where the SOC change before forestation was measured.Laganire et al. [2010] reported that SOC accumulation will be overestimated 12.4% by nonretrospectivemethods compared with retrospective methods. Based on an investigation of 159 plots in northeasternChina, Wang et al. [2011] reported that a similar difference in the magnitude of SOC dynamics wasobserved in sites using nonretrospective methods compared to retrospective methods, but that thiswas caused by the failure to take deep enough soil samples to assess SOC change after forestation.Therefore, poor quality in experimental methods will introduce bias into fittedmodels that are establishedbased on experimental data.

2. Lack of measurement of C accumulation in the O horizon. Forest soil profiles consist of the O horizon (forestfloor) and the mineral soil layer [Laganire et al., 2010; Thuille and Schulze, 2006]. C accumulation in theO horizon is a crucial part of the integrated soil profile [Thuille and Schulze, 2006]. A long-term resamplingexperiment indicated that up to 20% of the total forest C sink was observed in the O horizon, whileonly

BD according to a fitted line of SOC concentration and BD, this approach cannot eliminate the bias due tomissing BD data. In addition, our estimations focused on SOC change after restoration in the top 20 cm soillayer and did not calculate the SOC change below this depth. SOC change in forested land at greaterdepths is a debated topic, with estimates of SOC ranging from significant depletion to dramatic increase.Based on a global meta-analysis of deep SOC change, Shi et al. [2013] found that the SOC change rate inthe 060 cm profile was 1.49 times greater than that in the corresponding 020 cm layer. Therefore,shallow sampling depth is likely to have underestimated the SOC sequestration potential of the GGP.

5. Disparity between the distribution of sampling sites and regions of restoration. The disparity between thegeographic distribution of study sites and regions of land-use change introduces a bias into theestimation of SOC sequestration [Powers et al., 2011]. Previous sampling sites were mainly located in theLoess Plateau (Gansu, Shaanxi, and Shanxi) where soil and water had suffered from erosion. There wererelative fewer sampling sites for forestation on barren land in northern regions and for forestation bynatural succession in the southwestern and northeastern regions. However, these areas are also majorarea of GGP implementation. Thus, the disparity between the geographic distribution of study sites andregions of restoration contributes to uncertainty in the results.

6. Tree species and types of forestation. Net primary productivity, occurring through photosynthesis, is thesource of C sequestered in soils of afforested land. There was a large discrepancy in the productivityof tree species located on restoration sites [Xu et al., 2010]. Laganire et al. [2010] reported that the rate ofSOC accumulation under broadleaf species was greater than that under coniferous species. Nitrogen-fixing plant species can also substantially add to the amount of available N in soil through biologicalnitrogen fixation [Binkley, 2005; Resh et al., 2002]. The increase in N can then improve the productivity ofplantations and decrease both soil and microbial respiration rates, thus facilitating C sequestrationand improving soil fertility in forested lands [Resh et al., 2002]. This study divided the forestation zones ofthe GGP mainly based on forest and climate zoning, and did not consider specific tree species or theuse of N-fixing species as part of the zoning process.

4.2. Implications

Assessment of the soil C budget at large regional or national scales is difficult [Schulze et al., 2010] but criticalfor China given the large-scale forestation efforts over the last decades [Liu and Tian, 2010]. Althoughsome previous assessments have quantified SOC change rates or SOC stock changes after restoration oncropland (one component of the GGP) and its potential determinants at national scales [Zhang et al., 2010;Deng et al., 2013; Zhao et al., 2013; Song et al., 2014], the temporal dynamics of soil C sequestrationpotential in each forest zone and each type of restoration supported by the GGP has never been analyzedsystematically using a statistical model. In our study, based on the results of an extensive field experimentand a thorough literature survey, we established a relationship between the response of SOC changeand plantation age in the GGP using a linear mixed model for planted forest and a logistic model forgrassland, and estimated the change rate in SOC stock using a weighted meta-analysis. We estimated thatsoil C sequestered by GGP activities was 156 Tg C as of 2013 and predicted that this potential will grow to383 Tg C by 2050. There are three main implications of these findings.

First, this study of GGP effects on forest soils has expanded our knowledge of the C sink in forestedecosystems. Many previous studies focused on C sequestration in biomass and considered that the Csequestered in soil is negligible [Wu et al., 2008; Persson et al., 2013]. This study showed that restored soil has ahuge potential for C accumulation in the northwestern, southern, and southwestern regions but less so innortheastern China (Figure 6). The GGP has been implemented on degraded land that suffered fromwater and soil erosion. These lands can sequester C by increasing net primary productivity, which allows forgreater C input into soil, which is then retained as soil organic matter [Degryze et al., 2004; Poeplau and Don,2013]. Recently, Persson et al. [2013] estimated that forest established by the GGP on 20 Mha throughforestation on cropland and barren land sequestered 222468 Tg C in aboveground and belowgroundbiomass between 1999 and 2008, with a mean annual sequestration rate of 28.9 Tg C yr1. In this study, atotal of 24.3 Mha forest established by forestation on cropland and barren land sequestered 57227 Tg C insoils during 19992012 (Figure 6), with a mean annual rate of 10.9 Tg C yr1. By comparing the meanannual rate of C sequestration per area, we can estimate that the total amount of C sequestered in soils byforestation on cropland and barren land since the start of the GGP accounts for almost 24% of the total C sink



of the established forest ecosystems.Similarly, Janssens et al. [2003] reportedthat the soil C sink in Europeanforest ecosystems accounted for about30% of the total sink.

Second, our analysis indicates potentialoptions for ways to increase soil Csequestration in the GGP. According tothe Forestry Statistics Yearbook, 80% ofthe forested lands of the GGP wasestablished during 19992006 (Figure 8)(Table S4), indicating that as of 2013 mostof the restored land had a plantation agegreater than 5 years. SOC loss oftenhappens in the early stages (15 years)after forestation [Paul et al., 2002; Zhanget al., 2010; Deng et al., 2013], due to lowerC input rates and decomposition causedby soil disturbance. The rate of input ofSOC increases with plant productivityand plantation age [Dewar and Cannell,1992], and SOC accumulation will beaccelerated by plantation growth.Generally, most of the tree species in aplanted forest (e.g., Cunninghamialanceolata, Pinus massoniana, and Populussimonii) mature within 2040 years[Xu et al., 2010]. Therefore, an establishedplantation will be in a stage of rapidC sequestration in biomass and soils. Wesuggest that the existing plantationareas of the GGP should be conservedwell into the next 20 years.

Lastly, our analysis provides suggestionsfor the future implementations of ChinasGGP. There is great variation in soil

C sequestration among different zones and restoration types. Soil C sequestration under forestation oncropland and barren land in the northwestern, southern, and southwestern regions was significantlyhigher than in the northeastern region of China (Table 1). In contrast, when natural succession is adoptedas the type of forestation, the soil C stock in the northeastern region can increase rapidly, with anaccumulation rate of 1.54Mg C ha1 yr1. Interestingly, regression analysis shows that the mean annualSOC change rate after forestation increased with mean SOC content in the reference sites across thedifferent forest zones, except for in planted forests in the northeastern region (Figure 9). SOC contentdynamics after forestation is determined by the relative rates of inputs and outputs [Li et al., 2012]. Thehigher SOC accumulation rates we found in the southern and southwestern study regions compared tothe northwestern region might then be explained by the relatively higher plant productivity of subtropicalforest ecosystems, owing to favorable natural conditions (e.g., increased nitrogen deposition, sufficientwater, and heat availability) [Yu et al., 2014], even if the mean SOC content in reference plots in thesouthern and southwestern regions was higher than that in the northwestern region. The forest formed bynatural succession involves minor soil disturbance, while planted forest increased soil disturbance (e.g., sitepreparation or manipulation) resulting in increasing risk of decomposition of old C in soils if the soilshas a higher initial C content [Jandl et al., 2007]. Therefore, forest formed by natural succession has a higherC sequestration potential than planted forest when the soil initial C content is extremely high, and

Figure 9. Relationship between mean SOC content in the referencesite and mean annual change rate in SOC stock after forestationaccording to meta-analysis results in Table 1. (a) Planted forestsestablished on cropland and barren land and (b) forests formedthrough natural succession (error bars indicate the 95% confidenceinterval). Abbreviations as shown in Table 1.



minimizing soil disturbance should be taken into consideration when promoting planted forestexpansion in the northeastern region.

Our study concluded that soil C sequestration due to the GGP is an important part of the terrestrial C sink inChina. To our knowledge, this study was the first time an evaluation of the soil C sequestration potential ofthe GGP in China was performed, which provides information for a better understanding of the C sink ofkey forestry projects. The results of this study are affected by many sources of uncertainty. Future researchshould assess C accumulated in the O horizon and deep soil layers in order to develop amore comprehensiveunderstanding of the contribution of forestation projects to the mitigation of global climate change throughthe C cycle.

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AcknowledgmentsData supporting for meta-analysis(Table 1 and Figure 9) and statisticalmodel establishment (Tables 2 and 3,and Figures 4, 5, and 8) are available inTables S1S4 in the supportinginformation. This research was spon-sored by the National Key FundamentalResearch Program for the ChineseScience and Technology Department(2010CB950604). Many thanks to YaoHuang, Wen Zhang, and two anonymousreviewers who provided suggestions forimproving this manuscript, Ping Zhang,Lijun Yu, and Haiping Wei at the Instituteof Atmospheric Physics, and Fan Ding,Chenglin Ma, and Yue Chen at theInstitute of Botany of CAS for theircontribution in field investigation andlaboratory experiments. We would alsolike to thank Andreas Joshua Wilkes inValues for Development Ltd for his assis-tance with English language and gram-matical editing of the manuscript.

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estimating the soil carbon sequestration potential of china's grain for green project

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