effects of ecological restoration-induced land-use change and improved management on grassland net...

Effects of ecological restoration-induced land-usechange and improved management on grassland netprimary productivity in the Shiyanghe River Basin,north-west China

W. Zhou, J. L. Li, S. J. Mu, C. C. Gang and Z. G. Sun

School of Life Science, Nanjing University, Nanjing, China

Abstract

To address severe grassland degradation, the Chinese

government implemented national restoration pro-

grammes, which in turn drove a research focus

towards assessment of the environmental effectiveness

of such initiatives. In this study, net primary produc-

tivity (NPP) was used as an indicator for assessing the

impacts of land use and cover change (LUCC),

improved land-use management and climate change

on the grassland ecosystem of the Shiyanghe River

Basin. NPP was calculated on the basis of the Carne-

gie–Ames–Stanford Approach model, which is driven

by a Moderate Resolution Imaging Spectroradiometer

(MODIS) normalized difference vegetation index and

meteorological data. The LUCC data for 2001 and

2009 were derived from MODIS land-cover data. Dur-

ing the study period, the net increase in grassland

development was 5105�5 km2, with 80�4% of the

newly developed grasslands attributed to desert-

to-grassland conversion. The total NPP of grasslands in

2009 increased by 659�62 Gg C compared with that in

2001. The contributions of human activity and climate

change to total NPP increase were 133 and �33%

respectively. Land conversion and improved manage-

ment measures directly increased grassland NPP. These

factors are dominant positive driving forces, whereas

warm and dry climates impose adverse effects on

grassland restoration in the study site.

Keywords: grassland net primary productivity,

ecological restoration programmes, land use and cover

change, climate change, Shiyanghe River

Introduction

Human activities, primarily agricultural expansion,

urban sprawl and economic development, have chan-

ged about half of the Earth’s land surface through land

use and cover change (LUCC) (Vitousek et al., 1997).

These activities have considerably affected ecosystem

structure, function and diversity (Foley et al., 2005; Pi-

elke, 2005; Yan et al., 2009). As one of the world’s

most widespread vegetation types, grassland accounts

for nearly 20% of the world’s land surface (Scurlock

and Hall, 1998). It has been acutely influenced by

human activities, such as food production and animal

husbandry development (Conant et al., 2001). China’s

grasslands cover an area of 3�93 million km2, account-

ing for about 40% of the country’s total land area, 6–8% of the world’s total grasslands and 9–16% of the

world’s grassland carbon stocks (Ni, 2002). However,

the grasslands in northern China are susceptible to

degradation (Nan, 2005) because of climate warming

trends and land-use intensification (Kang et al., 2007),

exacerbated by population growth and socio-economic

development. The degraded grassland area in northern

China amounts to 6700 km2 each year (Yang, 2002).

This degradation has led to productivity decline, land

degradation and dust storm increase.

Adverse human activities, such as the overexploita-

tion of surface water and ground water for irrigation,

overgrazing and grassland-to-cropland conversion,

caused large-scale land degradation across the Shiyan-

ghe River Basin (Zhang et al., 2012). Shiyanghe River,

located at the eastern Hexi corridor of north-west

China, is an ecologically vulnerable area. The Shiyan-

ghe River Basin has seen increasing grassland-to-crop-

land conversion since 1940, a trend that has further

intensified, especially with the rapid population

growth and economic development in the 1980s (Xie

et al., 2004). Although land reclamation guarantees

food safety, grazing pressure simultaneously increases,

and the ensuing overexploitation of grasslands leads to

wind erosion and land degradation.

Correspondence to: J. L. Li, School of Life Science, Nanjing

University, Hankou Road 22, 210093 Nanjing, China.

E-mail: [email protected]

Received 1 July 2012; revised 16 April 2013

596 © 2013 John Wiley & Sons Ltd. Grass and Forage Science, 69, 596–610 doi: 10.1111/gfs.12073

Grass and Forage Science The Journal of the British Grassland Society The Official Journal of the European Grassland Federation

Land degradation causes serious environmental

and social problems, including decreased biological

productivity, declining soil quality, loss of biodiversity

and increased sandstorm occurrence (LeHouerou,

1996). Such degradation also decreases carbon seques-

tration and increases the net release of carbon dioxide

into the atmosphere (Millennium Ecosystem Assess-

ment, 2003); this negative effect ultimately influences

the carbon cycles of ecosystems and restricts the sus-

tainable development of Chinese economy and society

(Xu et al., 2011a). To mitigate the impact of desertifi-

cation and environmental degradation, therefore, the

Chinese government initiated several ecological resto-

ration programmes in the late 1990s and early 2000s.

These programmes include the Natural Forest Conser-

vation Program, Grain to Green Program (GTGP) and

Returning Grazing Land to Grassland Program

(RGGP). The GTGP and RGGP were implemented in

Gansu Province, with particular emphasis on grassland

protection and restoration. The GTGP, the purpose of

which is ‘converting cropland to forest and grassland

in fragile areas’, is the largest programme in China

and worldwide in terms of scale, budget and duration

(Ferraro and Kiss, 2002; Wang et al., 2007). The GTGP

was piloted in three provinces (Sichuan, Shanxi and

Gansu) in 1999 and then expanded to twenty-five

provinces in 2002 (Liu et al., 2008). As a complement

to the GTGP, the RGGP was launched in 2003. It

focuses on alleviating grazing pressure in the degraded

grasslands of north-west China by forbidding grazing,

implementing rotational grazing or converting grazing

land to cultivated pasture (Tong et al., 2004). These

two restoration programmes prompted improvements

to the measures designed for land-use management. A

number of studies on the impact of such programmes

on China’s grassland ecosystem have been published

(Liu et al., 2008; Wang et al., 2011). In Hunshadake

sandy land of Inner Mongolia, previously degraded

grasslands were rapidly restored after 3 years of enclo-

sure (Jiang et al., 2006). Besides, grasslands obtained

obvious restoration after the implementation of the

RGGP in Maqu County of South Gansu Province

(Wang et al., 2009a). By contrast, quantitative assess-

ments of the impacts of ecological restoration projects

on the LUCC and grassland productivity in the Shi-

yanghe River Basin are limited.

Net primary productivity (NPP) is the net amount

of solar energy converted to chemical energy through

photosynthesis (Imhoff et al., 2004). It is an important

parameter of ecosystem function and a key indicator

of global carbon cycles (Wang et al., 2007). This

parameter is also a sensitive indicator of climate

changes and human activities (Schimel, 1995) and is

becoming increasingly relevant to the formulation and

implementation of land-use policies and management

measures (Feng et al., 2007). Land-use patterns influ-

ence vegetation distribution and NPP (Gao et al.,

2003). Therefore, NPP can be used to quantify the

impact of LUCC across a broad spectrum of issues in

earth-system science and global-change research (Xu

et al., 2007). Some studies have recently been con-

ducted to analyse the response of NPP to LUCC or cli-

mate change (Gao et al., 2004; Wang et al., 2009b;

Yan et al., 2009; Xu et al., 2011b). However, the spe-

cific effects of climate and human factors on NPP

remain unclear because researchers assume that

climates or land-use types remain unchanged as their

impacts are assessed. NPP is influenced by both

climate and human intervention, there has been little

research to distinguish but the individual contributions

of these factors to vegetation NPP.

In this study, we designed a method for quantita-

tively assessing the individual effects of climate

change, LUCC and management measures on grass-

land NPP. The Thornthwaite memorial model (Lieth

and Box, 1972) was used to estimate potential NPP,

which is determined only by climate. It is used to

assess the impact of climate on potential NPP changes

in the present study and also provides a method to

discriminate the impacts of human activities on NPP

change. The Carnegie–Ames–Stanford Approach

(CASA) is a terrestrial ecosystem model driven by

remote sensing vegetation index and climate data

designed for vegetation NPP estimation (Potter et al.,

1993, 2009); it is also extensively used to simulate

grassland NPP in China (Piao and Fang, 2002; Piao

et al., 2006; Gao et al., 2013). We used the CASA

model to estimate actual grassland NPP. By studying

the response of grassland NPP to climate change and

human activities, we can better understand the eco-

system functions of grassland, as well as provide rec-

ommendations for future policies on grassland

restoration and sustainable development projects for

grassland ecosystems.

This study aims to (i) investigate LUCC in the Shi-

yanghe River Basin from 2001 to 2009; (ii) evaluate

the effects of LUCC and improved management under

the ecological programmes on the grassland NPP of

the region; and (iii) assess the individual effects of

human activities and climate change on grassland

NPP, as well as determine which between the two is

the dominant factor.

Materials and methods

Study area

The Shiyanghe River Basin is located in north-west

China at the east of the Hexi corridor (31°32′N to

49°10′N and 73°15′E to 111°50′E). Administratively,

© 2013 John Wiley & Sons Ltd. Grass and Forage Science, 69, 596–610

Effects of land use change and improved management on grassland NPP 597

the basin includes parts of Qilian County in Qinghai

Province and some counties and cities of Gansu Prov-

ince. This basin occupies an area of about

4�16 9 104 km2. The Badain Jaran and Tengger

deserts surround the region along its western, north-

ern and eastern margins (Figure 1). The formation

and evolution of the basin are controlled by the evo-

lution of the Shiyanghe River and its tributaries,

which originate from the eastern part of the Qilian

Mountains. The south-west section of the basin

belongs to the Qilian Mountains region, with an ele-

vation decrease from 5000 to 2000 m, corresponding

to the decrease in annual precipitation from 600 to

300 mm. The central section covers areas with

altitudes ranging from 1400 m to 2000 m and precipi-

tation ranging between 150 and 300 mm. The north-

east section covers areas with an elevation ranging

from 1000 to 1400 m and precipitation usually

<120 mm. The vegetation distribution presents obvi-

ous vertical zonality, including alpine meadows, forest

thickets, desert vegetation and oases, which can be

divided into the southern mountain ecological system,

the central plains desert and oasis ecological system,

and the northern desert (Guo et al., 2010). The ecolog-

ical environments of this basin are very vulnerable

because of their low precipitation, high evaporation

and potentially severe sand transport (Ma et al.,

2005a). Furthermore, the Shiyanghe River Basin is an

important sandstorm source in China (Wang et al.,

2004). Minqin County along the lower reaches of the

basin has a higher frequency of severe sand and dust

storms than in any other part of China (Qian et al.,

2002). Pessimistically, Minqin could become China’s

second Lop Nur, another famously degraded area in

north-west China (Dong et al., 2010), because of

climate warming and the intensification of human

interference. At present, the health of the environ-

ment and local population is greatly threatened by the

rapid reductions in groundwater, vegetation degenera-

tion and more frequent sandstorms. Therefore, the

Shiyanghe River Basin, with its poor climate condi-

tion, is currently considered to be a typical research

region in land degradation.

Data source and processing

Remote sensing [normalized difference vegetation

index (NDVI) and land cover data], meteorological

and geographical data were obtained to estimate the

NPP and investigate the LUCC in the basin. The

remote sensing data sets include 500 m 16-d Moderate

Resolution Imaging Spectroradiometer (MODIS)-NDVI

(MOD13A1) data and MODIS global land cover prod-

uct with a spatial resolution of 500 m (MCD12Q1).

We chose 2001 to 2009 data, obtained by the MODIS

sensor on board NASA’s Terra satellite. The data are

readily available at http://ladsweb.nascom.nasa.gov/

data/search.html. The maximum-value composite pro-

cedure was used to merge 16-d NDVI data and gener-

ate monthly NDVI data sets. These remote sensing

data were reprojected from the original Integerized

Sinusoidal Projection to an Albers equal area and

WGS-84 datum by using ArcGIS V9.3 (ESRI, CA,

USA).

Figure 1 Current distribution of

the Grain to Green Program

(GTGP) and Returning Grazing Land

to Grassland Program (RGGP) in

China (Liu and Diamond, 2005;

Ouyang, 2007). The names of coun-

ties in the Shiyanghe River Basin are

shown on the location map. The

land cover data are based on the

Moderate Resolution Imaging Spect-

roradiometer land cover product.

GTGP indicates the areas where

only this programme was imple-

mented, GTGP and RGGP indicates

the locations where both pro-

grammes were implemented, and

No GTGP or RGGP indicates the

regions where no programme was

implemented.


598 W. Zhou et al.

The MODIS global land cover product is based on

a supervised classification system with a decision-tree

classifier (Friedl et al., 2002). This MODIS land cover

data set is equivalent to the IGBP DISCover global

1-km land cover data set and includes the same sev-

enteen land cover types (Wu et al., 2008). MCD12Q1

also includes eleven natural vegetation classes, three

developed and mosaic land classes and three non-

vegetated land classes. The overall classification accu-

racy of MCD12Q1 land cover data for all categories is

estimated to be 74�8% globally, with a 95% confi-

dence interval of 72�3–77�4% (Friedl et al., 2010). The

global accuracy estimates of the IGBP layer of

MCD12Q1 for Inner Mongolia, China, are as follows:

66% for grassland, 58% for cropland, 85% for open

shrubland, 65% for mixed forest and 74�5% for bar-

ren land (John et al., 2009). We directly used the

MODIS land cover product to create the 2001 and

2009 land cover map, for which the seventeen classes

were reclassified into seven categories (Ran et al.,

2010): (i) water bodies, (ii) forest, (iii) grassland, (iv)

cropland, (v) urban and built-up land, (vi) cropland/

natural vegetation mosaic and (vii) desert. Within

these categories, evergreen and deciduous needleleaf

forests, evergreen and deciduous broadleaf forests,

mixed forests and closed shrublands were reclassified

under forest. Open shrublands, woody savannahs,

savannahs, grasslands and permanent wetlands were

reclassified under grassland. The land cover map of

2001 and 2009 was used to analyse LUCC and its

impact on NPP changes.

Meteorological data

Meteorological data were obtained from the China

Meteorological Science Data-Sharing Service System.

The data include the monthly average temperature

and total precipitation recorded by 18 meteorological

stations, as well as total solar radiation recorded by 10

stations, in and around the Shiyanghe River Basin

from 2001 to 2009. Ordinary kriging interpolation was

performed to interpolate the meteorological data

intended for producing raster images with 500 m spa-

tial resolution. These monthly meteorological data

were used to drive the CASA model.

The meteorological data used to drive the Thorn-

thwaite memorial model include annual total precipi-

tation and average temperature. The annual total

precipitation is the sum of the 12-month precipitation;

annual average temperature is the mean value of 12-

month temperature. Raster meteorological data were

also extracted using the vector boundary of the study

area. The images with the same spatial resolution and

coordinate system as those used in the CASA model

were used for remote sensing.

Field survey on NPP

We sampled thirty-six sites across the Shiyanghe River

Basin in July 2009 (Figure 1). At each site

(10 m 9 10 m), all the plants in five plots

(1 m 9 1 m) were harvested to determine above-

ground biomass. To determine under-ground biomass,

nine soil cores (8 cm diameter) were used to collect

samples at 10-cm intervals. Root samples were imme-

diately placed in a cooler and then transported to the

laboratory. The samples were soaked in deionised

water and cleaned of soil residue using a 0�5-mm

mesh sieve. The biomass samples were oven-dried at

65 °C to a constant mass and weighed to the nearest

0�1 g. The biomass was subsequently converted into

carbon content by using a conversion factor of 0�45(Fang et al., 1996). These field observation data were

used to verify the accuracy of the NPP estimated by

the CASA model.

Methods

Estimation of actual NPP by the CASA model

Vegetation dynamics are critical to the LUCC process

because they reflect the complex interactions between

climate change and human activities (Hanafi and Jauf-

fret, 2008). In this study, annual NPP (g C

m�2 year�1) was used to represent vegetation condi-

tions and assess the individual effects of climate

change and human activities on the grassland ecosys-

tem of the basin.

Actual NPP was calculated using the CASA model,

a light-use efficiency model based on resource balance

theory (Potter et al., 1993; Field et al., 1995). In the

CASA model, NPP is the product of absorbed photo-

synthetically active radiation (APAR) and light-use

efficiency (e) (Potter et al., 1993). The basic principle

of the model is described as follows:

NPP x; tð Þ ¼ APAR x; tð Þ � e x; tð Þ; ð1Þ

where x is the spatial location (pixel number), t is

time, APAR represents the canopy-absorbed incident

solar radiation integrated over a given time period

(MJ m�2) and (x, t) represents the actual light-use

efficiency (g C MJ�1). APAR(x, t) and (x, t) are calcu-

lated using Equations (2) and (3) (Wang et al., 2009a,b;

Yu et al., 2011):

APAR x; tð Þ ¼ SOL x; tð Þ � FPAR x; tð Þ � 0�5; ð2Þ

where SOL (x, t) is the total solar radiation (MJ m�2)

of pixel x in time t, and FPAR(x, t) is the fraction of

the photosynthetically active radiation absorbed by



vegetation. FPAR(x, t) can be determined by NDVI; 0�5represents the proportion of the total solar radiation

available for vegetation (wavelength range of 0�38–0�71 lm). The algorithm for light-use efficiency can be

expressed as:

e x; tð Þ ¼ Te1 x; tð Þ � Te2 x; tð Þ �We x; tð Þ � emax; ð3Þ

where Te1(x, t) and Te2(x, t) denote the temperature

stress coefficients, Te1(x, t) represents the influence of

extreme temperature on light-use efficiency (Field

et al., 1995), Te2(x, t) reflects the decrease in light-use

efficiency when temperature deviates from the opti-

mal level (Potter et al., 1993; Field et al., 1995), We2(x, t)

is the water-stress coefficient that indicates the reduc-

tion in light-use efficiency caused by moisture factor,

and emax denotes the maximum light-use efficiency

under ideal conditions set as different constant

parameters for various vegetation types (Zhu et al.,

2006). A more detailed description of this algorithm

can be found in the study by Yu et al. (2011).

Estimation of potential NPP by the Thornthwaite

memorial model

Although researchers have developed several models

for estimating NPP, such models are based on different

climatic factors. The first widely used model, the

Miami model (Lieth, 1975), is derived from the least-

squares correlations between measured NPP data and

corresponding temperature and precipitation data. The

Thornthwaite memorial model was established on the

basis of the data used in the Miami model, but were

modified to include Thornthwaite’s potential evapora-

tion model (Lieth and Box, 1972). In the current

study, we simulated potential NPP using the Thorn-

thwaite memorial model, which is expressed as

follows:

NPP ¼ 3000 1� e�0�0009695 v�20ð Þh i

; ð4Þ

where NPP is the annual NPP (g C m�2 year�1), and mis the average annual actual evapotranspiration (mm).

The calculated equations are expressed thus:

V ¼ 1�05rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1þ ð1þ 1�05r=Lp Þ2 ; ð5Þ

L ¼ 3000þ 25t þ 0 �05t3; ð6Þ

where L is the annual average evapotranspiration

(mm), r is the annual total precipitation (mm), and t

is the annual average temperature (°C).

Validation of the CASA model

The CASA model was validated through the compari-

son of the NPP derived from a field survey in July

2009 and that modelled by CASA. The correlation

analysis of the observed and estimated NPP data

(R2 = 0�603, P < 0�001) indicates that the CASA model

exhibited reliable estimation accuracy (Figure 2).

However, the estimated data were slightly larger than

the field observation data, and the correlation between

the observed and modelled NPP data was relatively

low. Through resolution bias, we found that the spa-

tial resolution of the NPP estimated by CASA was

500 m 9 500 m, whereas that of the NPP estimated

by the field survey was 10 m 9 10 m. The large dif-

ference in spatial resolution led to the variance

between the estimated NPP and field observation NPP.

Design of the quantitative assessment method

Net primary productivity can be driven by numerous

factors, particularly climate change and human inter-

vention. We designed a method that distinguishes the

individual effects of the two factors on NPP variations.

We defined the formula NPPactual = NPPclimate +NPPhuman. NPPactual, which represents the actual

changes in NPP under the influence of climate change

and human activities; these changes can be modelled

by CASA. NPPclimate represents the NPP variations

caused only by climate change; such changes can be

represented by the Thornthwaite memorial model.

Figure 2 Validation of Carnegie–Ames–Stanford Approach

(CASA) model accuracy through the correlation analysis of

the estimated and field observation net primary productivity

(NPP) (g C m�2) in July 2009. Modelled NPP denotes the

NPP value calculated using the CASA model; observed NPP

is the field survey NPP data. P < 0�001 indicates a significant

correlation between the modelled and field survey NPP.


600 W. Zhou et al.

NPPhuman pertains to human-induced NPP changes,

including land conversion-induced NPP change and

management measures-induced NPP change. Grass-

land was categorized into three classes: unchanged

grassland for the period 2001–2009 (i.e. land-use type

was grassland both in 2001 and 2009); newly devel-

oped grasslands in 2009 compared with 2001 (i.e.

land-use type was not grassland in 2001, but land was

converted to grassland in 2009); and converted grass-

land (i.e. land-use type was grassland in 2001, but

was converted to other land-use types in 2009).

Analyses of the change trends of annual precipitation

and temperature

We used Equation (7) to calculate the slope of the lin-

ear time trend determined via ordinary least-squares

estimation:

Slope ¼9� P9

i¼1

i� CFi � ðP9

i¼1

iÞðP9

i¼1

CFiÞ

9� P9i¼1

i2 � ðP9

i¼1

iÞ2; ð7Þ

where i is 1 for year 2001, 2 for year 2002 and so on

until 2009; CF is the climatic factor that represents

annual total precipitation or average temperature; and

Slope is the slope of the linear regression of one vari-

able equation. This slope is the average annual increase

(or decrease) in climate indicator from 2001 to 2009.

Results

Grassland conversion under ecologicalrestoration programmes

According to the data on land-use changes (Figure 3,

Table 2), the Shiyanghe River Basin exhibited a net

increase in grassland development of 5105�5 km2 from

2001 to 2009. The newly developed grasslands occu-

pied 6829 km2, whereas the grasslands converted to

other land-use types (especially croplands) occupied

1723�5 km2. Among the various forms of land conver-

sion, desert-to-grassland, forest-to-grassland, cropland-

to-grassland and grassland-to-cropland conversions

dominated (Figure 3a and b). The codes for the types

of land conversions are shown in Table 1. The

substantial increase in grassland area was caused by

conversions from desert, forest and cropland, account-

ing for 80�4, 9�7 and 9�5% of the newly developed

grasslands respectively. Desert-to-grassland conversion

was the most significant land conversion during the

study period, contributing 5492�25 km2 to the increase

in grassland area. By contrast, grassland-to-desert con-

version was minimal. In the entire study area, the

cropland area converted to other land-use types from

2001 to 2009 amounted to 847�50 km2, of which 77%

was converted to grassland. Moreover, 1119 km2 of

grassland was converted back to cropland, which

resulted in a total grassland loss of 64�9%, the largest

for this type of land use. Similarly, 665�5 km2 of

forest was converted to grassland, and 416�5 km2 of

(a) (b)

0 25 50 100km

Legend

23 32 33 34 36 37 43 63 73

N

Figure 3 Grassland conversion in the Shiyanghe River Basin from 2001 to 2009: (a) spatial distribution of grassland conversion

and (b) areas where major grassland conversion occurred. The codes represent the land cover types, with definitions presented

in Table 1.



grassland was converted back to forest. The land con-

version from cropland/natural vegetation mosaic to

grassland reached equilibrium during the study period.

From 2001 to 2009, land-use type considerably

changed and marked spatial heterogeneity occurred

(Figure 3a). The most significant landscape change

was characterized by the following land conversions:

desert-to-grassland conversion in the central and

south-east sections of the basin; forest-to-grassland

conversion south-west of the basin near Qilian Moun-

tains; and cropland-to-grassland conversion in most

regions. Marked grassland-to-cropland conversion also

occurred in the central and southern parts of the

basin. Spatially, desert-to-grassland conversion

occurred primarily in the transitional zone between

grassland and desert, including the western and north-

ern Badain Jaran Desert and eastern Tengger Desert.

Effects of grassland conversion on NPP

The variations in grassland NPP between 2001 and

2009 are shown in Table 2. The total grassland NPP

was 2072�67 Gg C (1 Gg = 109 g) and 2732�29 Gg C

in 2001 and 2009, respectively, with the latter indicat-

ing an increase of 659�62 Gg C or 31�8% over the

2001 level. The mean NPP of the unchanged grassland

increased by 11�80 g C m�2 year�1, which in turn

increased total NPP by 113�43 Gg C, accounting for a

contribution of 17�2% to total NPP increase. The land

conversion between grassland and other land-use

types positively affected the increase in grassland NPP,

thereby leading to a net increase of 546�19 Gg C after

the converted grassland-induced NPP loss was sub-

tracted. During the study period, the newly developed

grasslands exhibited a total NPP increase of 930�86 Gg

C. The most significant contributions to this increase

were those provided by desert-to-grassland conversion

(580�47 Gg C) and forest-to-grassland conversion

(199�57 Gg C). By contrast, the conversion from grass-

land to other land-use types (converted grassland) led

to a loss of 384�66 Gg C; the conversion from grass-

land to cropland caused a loss of 235�64 Gg C, making

it the dominant contributor to NPP decrease. There-

fore, the newly developed grasslands sufficiently com-

pensated for the NPP loss caused by the converted

grasslands.

Table 1 Codes for the conversions from grassland to other land-use and cover types from 2001 to 2009.

Forest to grassland (23) Unchanged grassland (33) Cropland to grassland (43)

Cropland/natural vegetation

mosaic to grassland (63)

Desert to grassland (73) Grassland to forest (32)

Grassland to cropland (34) Grassland to crop/natural vegetation mosaic (36) Grassland to desert (37)

The codes for land cover types are as follows: 2 denotes forest, 3 denotes grassland, 4 denotes cropland, 6 denotes cropland/

natural vegetation mosaic and 7 denotes desert. Codes 23, 43, 63 and 73 indicate forest, cropland, cropland/natural vegetation

mosaic and desert land cover types for 2001 respectively; these land types were then converted to grassland in 2009. Codes 32, 34,

36 and 37 indicate that the land cover type was grassland in 2001 and was then converted to forest, cropland, cropland/natural

vegetation mosaic and desert in 2009 respectively. Code 33 indicates that the land cover was grassland in 2001 and 2009.

Table 2 Area (km2), mean NPP (g C m�2 year�1), and total NPP (G g C = 109 g C) of different grassland conversion types in

the Shiyanghe River Basin between 2001 and 2009.

2001 2009

Codes Mean NPP Area Total NPP Codes Mean NPP Area Total NPP

Unchanged grassland Unchanged grassland

33 175�54 9616�00 1688�01 33 187�34 9616�00 1801�43Converted grassland in 2001 to 2009 Newly developed grassland in 2001 to 2009

32 290�79 416�50 121�11 23 299�88 665�50 199�5734 210�58 1119�00 235�64 43 222�75 651�00 145�0136 247�97 59�00 14�63 63 286�91 20�25 5�8137 102�95 129�00 13�28 73 105�69 5492�25 580�47Sum 11339�50 2072�67 16445�00 2732�29NPP, net primary productivity.Mean NPP is the annual mean NPP of one land cover type, and total NPP is the annual total NPP

of one land cover type. The codes are the same as those defined in Table 1.


602 W. Zhou et al.

Individual effects of human activities andclimate change on variations in grassland NPP

In accordance with the quantitative assessment

method designed in this study, we quantitatively

assessed the individual effects of climate change, LUCC

and improved management measures on the changes

in grassland NPP (Figure 4). The total grassland NPP

in 2001 comprises two elements: total NPP of the

unchanged grassland from 2001 to 2009 (green bar in

Figure 4) and the NPP of the converted grassland

(orange bar in Figure 4). The total NPP of grassland in

2009 also comprises two components: the total NPP of

the unchanged grassland and the NPP of the newly

developed grassland. The net increase in grassland

NPP includes the NPP changes induced by human

activities and climate change. The net variations in the

land conversion-oriented NPP (grey bar in Figure 4)

were calculated only for the newly developed grass-

land. These variations were estimated by subtracting

the NPP loss caused by grassland conversion to other

land-use types (orange bar in Figure 4). Climate

change reduced the NPP in the newly developed grass-

land (red bar in Figure 4). Improved management

increased grassland NPP (purple bar in Figure 4),

which was calculated only for the unchanged grass-

land by subtracting the total NPP value of that in 2001

(green bar in Figure 4). Climate change also reduced

NPP in the unchanged grassland (blue bar in

Figure 4).

Table 3 shows that the grassland NPP induced by

climate change decreased by 215�17 Gg C during the

study period, whereas that induced by human activi-

ties drastically increased by 874�79 Gg C or 133% of

NPP net increase (659�62 Gg C). Of this increase,

654�82 Gg C is attributed to the newly developed

grasslands after the NPP loss induced by converted

grassland was subtracted; 219�97 Gg C is attributed to

improved management. Therefore, land conversion

accounts for the most significant positive effects on

the increase in grassland NPP, whereas climate change

accounts for the most significant negative effects on

NPP increase. In the newly developed grasslands,

climate change reduced NPP by 108�63 Gg C, and

conversion from other land-use types to grassland

increased NPP by 1039�49 Gg C. This significant

growth could sufficiently counteract the NPP loss

caused by grassland conversion to other land-use types

(384�67 Gg C). In the unchanged grassland, �93�9%of total NPP increase was caused by climate change

and 193�9% was caused by improved management

measures, such as the prohibition of grazing or imple-

mentation of rotational grazing.

The spatial distributions of the individual effects of

climate change, LUCC and improved management on

–500

0

500

1000

1500

2000

2500

2001 2009 unchanged grassland

2009 newly developed grassland

NPP loss induced by grassland converted in 2001LUCC-induced NPP increaseClimate-induced NPP decrease in newly developed grasslandImproved management-induced NPP increaseTotal NPP of unchanged grassland in 2001Climate-induced NPP decrease in unchanged grassland

NP

P (G

g C

yea

r–1)

Figure 4 Total net primary productivity (NPP) of unchanged grassland, newly developed grassland and converted grassland in

2001 and 2009, with contributions of land use and cover change (LUCC), improved management and climate change. LUCC-

induced grassland NPP increase (grey bar) represents the net increase in NPP caused by the newly developed grasslands after

the NPP loss caused by grassland conversion to other land-use types (orange bar) was subtracted. Improved management-

induced NPP increase (purple bar) represents the management measures that increased grassland NPP (e.g. ban on grazing and

implementation of rotation grazing). Climate-induced NPP decrease shows that climate change adversely affected grassland NPP

increase in both unchanged grassland (blue bar) and newly developed grassland (red bar).



NPP exhibited obvious spatial heterogeneity (Fig-

ure 5). Although climate change posed positive effects

on the NPP increase in the western region of the study

area, its negative effects were greater than its positive

effects and were widespread in the south-east and

central regions (Figure 5b). Nevertheless, the negative

effects of climate change were counteracted by human

activities (Figure 5c and d) because the LUCC and

management measures increased the NPP in the

south-east and central regions. Furthermore, the posi-

tive effects of human activities on the increase in

grassland NPP in the aforementioned regions were

greater than their negative effects on the NPP in the

south-west region near Qilian Mountains. Under the

mutual influence of climate change and human activi-

ties, therefore, the overall trend of actual grassland

NPP was an increase in most regions (Figure 5a).

Discussion

Discussion of method

Global climate change continues to accelerate, making

the NPP an indispensable index for measuring ecosys-

tem responses and human activities, such as ecosystem

management (Potter et al., 1993). As the most obvious

form of human activity, LUCC can alter ecosystem

environments, consequently affecting vegetation NPP

(Imhoff et al., 2000; Gao et al., 2003). Several studies

have addressed this issue (Gao et al., 2004; Wang et al.,

2009b; Yan et al., 2009; Xu et al., 2011b), but the

quantitative assessments in these investigations were

conducted under the assumption that climate remains

unchanged as the contributions of LUCC and climate

change to NPP are evaluated (Gao et al., 2004; Wang

et al., 2009b). NPP is affected by both climate change

and human activities, making a quantitative assess-

ment of the impact of these two factors on NPP a

necessity. Such assessments identify the dominant

driving factor of the changes in ecosystem productiv-

ity. In the current work, we designed a method for

assessing the individual effects of climate change,

LUCC and improved management on grassland NPP.

The Thornthwaite memorial model was used to repre-

sent potential NPP, which serves as an indicator for

assessing the impact of climate change on NPP and

distinguishing it from the effects of human activities.

In the quantitative assessment, grassland was catego-

rized into three classes: unchanged grassland, newly

developed grassland and converted grassland.

The contributions of human activities and climate

change to the net increase in the grassland NPP were

133 and �33% respectively. In most of the regions of

the study area (Figure 5b), NPP decreased with

increasing temperature and declining precipitation.

This finding is consistent with that of Cheng et al.

(2008), who concluded that the climate warm–drytrend remains substantial in the eastern part of Qilian

Mountains, thereby decreasing vegetation cover in the

Shiyanghe River Basin.

Land use and cover change led to an increase in

grassland development of 5105�5 km2 from 2001 to

2009. The newly developed grasslands caused a net

NPP increase of 654�82 Gg C after the converted grass-

land-induced NPP loss was subtracted. Improved man-

agement measures, such as the prohibition of grazing,

implementation of rotational grazing or conversion of

grazing land to cultivated pasture, increased net NPP

to 219�97 Gg C. This result is confirmed by Wang et al.

(2009a), who found that the grasslands were

obviously restored after the implementation of the

RGGP in Maqu County of South Gansu Province.

Table 3 Effects of human activities and climate change on the changes in total NPP (Gg C = 109 g C) for different grassland

conversion types in the Shiyanghe River Basin between 2001 and 2009.

Codes Total NPP (2009)

Climate-induced

change NPPaLUCC Total NPP (2001) NPPhuman

LUCC-induced Newly developed (gain) Converted (loss)

23 199�57 �2�31 201�8943 145�01 �11�83 156�8563 5�81 �0�49 6�2973 580�47 �94�00 674�46

Sum 930�86 �108�63 1039�49 384�67 654�82Improved management-induced Unchanged grassland

33 1801�43 �106�54 1688�01 219�97LUCC, land use and cover change; NPP, net primary productivity. NPPaLUCC represents the NPP increase induced by newly

developed grassland (i.e. other land cover types were converted to grassland). NPPhuman indicates the human activity-induced

NPP change, including LUCC and improved management. The code definitions are the same as those in Table 1.


604 W. Zhou et al.

A comparative analysis of the quantitative assess-

ment results derived by our method and other studies

for the Shiyanghe River Basin is shown in Figure 6.

Gao et al. (2004) and Wang et al. (2009b) found that

climate change induced NPP increases of 17 and 26%

respectively. By contrast, the current work reveals that

climate change decreased NPP by 33% (Figure 6).

Regions with declining precipitation and rising tem-

perature account for 82% of the total grassland in the

present study, and grass growth is primarily limited by

precipitation in arid and semi-arid rangelands (Bailey

and Brown, 2011). The results derived by the

proposed methods correspond with the actual situa-

tion in the Shiyanghe River Basin; they are also con-

sistent with previous findings, in which the warm–dryclimate decreased vegetation coverage (Cheng et al.,

2008), and the improved management under the

GTGP reduced land degradation in the basin (Liu et al.,

2008). The difference between our study and the pre-

vious two is as follows: Gao et al. (2004) defined the

anomaly of NPP data in two consecutive years as the

impacts of climate change on the NPP in areas charac-

terized by land-use change. Wang et al. (2009b)

assumed that temperature and precipitation remain

(a) (b)

(c) (d)

Figure 5 Impacts of land use and cover change (LUCC), improved management and climate change on net primary productiv-

ity (NPP) variations from 2001 to 2009 (unit: g C m�2 year�1); (a) total NPP change, (b) climate-induced NPP change, (c)

LUCC-induced NPP change and (d) NPP change induced by human management measures (the legend represents NPP varia-

tion). The high value above zero (blue) reflects the 2009 NPP increase over 2001 levels; the low value under zero (yellow)

reflects NPP decrease.



unchanged as the effects of LUCC and climate change

on NPP are evaluated. In areas where no changes in

land use are observed, however, the two studies attri-

bute the variations in NPP to climate change. This

assumption may be unreasonable because human

activities also contribute to NPP changes. These

assumptions may be unsuitable for the Shiyanghe

River Basin, where annual precipitation and tempera-

ture fluctuations are considerable. The results derived

with our method showed that improved management

contributed 34% of NPP increase (Figure 6). More-

over, the proposed method not only calculates the

impacts of climate change, LUCC and improved man-

agement on NPP variations, but also enables conve-

nient quantitative assessment based on remote sensing

images. Therefore, the proposed method is more reli-

able and efficient in quantitatively evaluating the indi-

vidual effects of climate change, LUCC and improved

management on the changes in grassland NPP in the

study area.

Effect of ecological restoration programmeson LUCC and improved management

The GTGP aims to increase vegetation coverage in

China by 32 9 104 km2, of which 14�7 9 104 km2 of

cropland on steep slopes is to be converted back to

grassland and forest (Ouyang, 2007). We found that

651 km2 of cropland was converted to grassland, and

1119 km2 of grassland was converted back to crop-

land. Grassland-to-cropland conversion remains a seri-

ous problem in the Shiyanghe River Basin, giving rise

to the need to enhance the effectiveness of ecological

restoration policies. If the government fails to achieve

this goal, the cultivation of new croplands may cause

desertification after several years of cropland use. The

selection of vegetation species is also important in veg-

etation restoration (Wang et al., 2007; Du et al., 2011).

Studies conducted on Yunnan Province (Chen et al.,

2009) and Zigui County of Hubei Province (Liu et al.,

2008) indicated that most of the croplands were con-

verted to tree plantations under the GTGP. In contrast

to such research, our study shows that most of the

croplands were converted primarily to grassland, as

evidenced by the unsuitability of trees for arid and

semi-arid regions; trees deplete water supply, thereby

exacerbating degradation (Gao and Liu, 2010). Indeed,

the annual precipitation in most regions of the Shiyan-

ghe River Basin is less than 300 mm. Thus, planting

drought-tolerant grasses and shrubs is a better choice

for long-term and sustainable ecosystem restoration.

Under the GTGP, 17�3 9 104 km2 of barren land

nationwide (particularly in the sandy lands near the

edge of an oasis) was allotted for grass planting from

1999 and 2010 (Chen et al., 2009). We also found that

at the Shiyanghe River Basin, 5492�25 km2 of desert

was converted to grassland from 2001 to 2009.

Land use and management measures substantially

govern the sustainability of a given land type (Foley

et al., 2005). Therefore, the decline in grazing intensity

and improvement in land-use management pro-

foundly affect grassland ecosystems and LUCC. Recent

surveys have shown that nearly 90% of the natural

grassland in China has been degraded to various

degrees – a phenomenon attributed primarily to over-

grazing (Liu et al., 2004). With the implementation of

the GTGP and RGGP, various efforts have been directed

towards alleviating grazing pressure to restore degraded

grassland. Wang et al. (2009a) reported that the RGGP

benefits grass growth and grassland restoration and

Figure 6 Comparative analyses of

the quantitative assessment results

for the Shiyanghe River Basin, as

derived by different methods (the

proposed method and those of Gao

et al. (2004) and Wang et al.

(2009b)).


606 W. Zhou et al.

that prohibiting grazing is a more effective measure

than implementing rotational grazing. Our findings

also reveal that improved management measures

increased NPP, accounting for 17�2% of net NPP

increase.

Impact of human activities on grassland NPPand ecosystem environment

Under vegetation restoration measures, the reduction

in human intervention increases carbon sequestration

(Gao and Liu, 2010). In this study, the conversion of

cropland and desert to grassland occurred mainly in

sandstorm sources, such as the Bardan Jaran Desert

and Tengger Desert. This conversion not only increased

grassland NPP, but also exerted a positive effect on

sandstorm control. Ma et al. (2011) reported that in

the past decades, sandstorms in the oasis–desert transi-tion zone in Minqin County have decreased. The GTGP

also enables water conservation and reduces desertifi-

cation in Minqin County. A previous study revealed

that 516 000 m3 of water resources were conserved in

2003 through the reduction in irrigation on 43 km2 of

GTGP land in Minqin County; the rate of desertifica-

tion has also dropped (Ma and Fan, 2005b).

Climate change and its impact on grasslandNPP

Overall, climate change restricts the increase in grass-

land NPP despite the rising trend observed in several

regions of the study area (Figure 5b). Recent research

has found that both the temperature and precipitation

in the Shiyanghe River Basin have increased during

the past 50 years (Du et al., 2011). Similarly, the pres-

ent findings show that the annual precipitation in the

western part of the basin near Badain Jaran Desert

exhibited an increasing trend during the study period

(Figure 7a). NPP also showed an increasing trend (Fig-

ure 5b), and the increase in precipitation benefited

vegetation growth, especially in dry land (Herrmann

et al., 2005). For the most part, however, the annual

precipitation in the basin showed a declining trend

(Figure 7a). The annual mean temperature increased,

especially in the regions near Qilian Mountains (Fig-

ure 7b). Our findings also confirm that NPP decreased

in the aforementioned regions because the rise in tem-

perature increased evaporation, which is harmful to

vegetation growth in dry land (Figure 5b). These

results are consistent with those of Cheng et al.

(2008), who indicated that the warm–dry trend of

climate remains considerable, thereby decreasing vege-

tation cover in the Shiyanghe River Basin.

Conclusion

From 2001 and 2009, the LUCC in the Shiyanghe

River Basin changed considerably. The region exhib-

ited a net increase in grassland development of

5105�5 km2 during the study period. The total NPP of

grassland increased from 2072�67 Gg C in 2001 to

2732�29 Gg C in 2009, with a net increase of

(a) (b)

Figure 7 Change in trends of (a) annual precipitation and (b) annual mean temperature, for which the slopes of precipitation

(mm) and temperature (°C) were calculated using Equation (7). A value above zero represents precipitation or temperature

increase in 2001 to 2009 and vice versa.



659�62 Gg C. The contributions of human activities

and climate change to the net increase in grassland

NPP were 133 and �33% respectively. The LUCC-

induced grassland NPP had a net increase of

654�82 Gg C; the improved management-induced NPP

increased by 219�97 Gg C; and the climate change-

induced NPP decreased by 215�17 Gg C. Human activ-

ity was the dominant positive factor for the increase

in grassland NPP, whereas the warm–dry trend of

climate was the dominant negative factor that

increased grassland NPP. This result demonstrates that

vegetation restoration programmes tremendously

influence LUCC and NPP increases. Land conversion

from desert and cropland to grassland is the dominant

driving force of the increase in grassland NPP.

Improved management measures, such as bans on

grazing, implementation of rotational grazing and con-

version of grazing land to cultivated pastures, are also

advantageous to vegetation restoration and NPP

increase. Therefore, the appropriate implementation of

the GFGP and RGGP alleviates grassland degradation

and improves China’s carbon sequestration potential.

In conclusion, the methods used in this study are

applicable to other regions where ecological restora-

tion programmes are launched. This study provides

important insights into the removal of atmospheric

carbon dioxide and the decline of sandstorms in China

and around the world.

Acknowledgments

We are grateful to the chief editor and anonymous

reviewers for illuminating comments. This work was

supported by National Basic Research Program of

China (2010CB950702) and the National High Tech-

nology Research and Development Program of China

(2007AA10Z231), the National Natural Science Foun-

dation of China (40871012), the Asia-Pacific Network

(ARCP-2013-16NMY-Li) and the Public Sector Link-

ages Program supported by Australian Agency for

International Development (64828). We are grateful

to Dr. Victor Squires from University of Adelaide,

Australia, for his help in modifying the language. We

also thank the China Meteorological Data-Sharing

Service System for granting us access to climate data

sets.

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